battery degradation

battery-degradation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "efficient-debut",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from scipy.io import loadmat\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "import h5py as st\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import mat4py\n",
    "import pickle\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import initializers, regularizers\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras.optimizers import Adam, RMSprop\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.layers import (\n",
    "    RepeatVector,\n",
    "    Input,\n",
    "    Normalization,\n",
    "    LSTM,\n",
    "    Dense,\n",
    "    Reshape,\n",
    "    Dropout,\n",
    "    ZeroPadding1D,\n",
    "    Masking\n",
    ")\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "import tensorflow.keras.backend as kb\n",
    "\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "57533589",
   "metadata": {},
   "outputs": [],
   "source": [
    "org_data = mat4py.loadmat(\"Degradation_Prediction_Dataset_ISEA.mat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "vertical-delaware",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['TDS'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "daily-monroe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(org_data[\"TDS\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1f41d151",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cell</th>\n",
       "      <th>Sample</th>\n",
       "      <th>History_Cycle</th>\n",
       "      <th>History</th>\n",
       "      <th>Target_Cycle_Expanded</th>\n",
       "      <th>Target_expanded</th>\n",
       "      <th>Target_Cycle</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...</td>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[105, 110, 115, 120, 125, 130, 135, 140, 145, ...</td>\n",
       "      <td>[1.7973466099593307, 1.7957194104838883, 1.794...</td>\n",
       "      <td>[105, 150, 195, 240, 285, 330, 375, 420, 465, ...</td>\n",
       "      <td>[1.7973466099593307, 1.7832393947563925, 1.770...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...</td>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[110, 115, 120, 125, 130, 135, 140, 145, 150, ...</td>\n",
       "      <td>[1.7957194104838883, 1.794106179214274, 1.7925...</td>\n",
       "      <td>[110, 155, 200, 245, 290, 335, 380, 425, 470, ...</td>\n",
       "      <td>[1.7957194104838883, 1.7817527911332156, 1.769...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...</td>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[115, 120, 125, 130, 135, 140, 145, 150, 155, ...</td>\n",
       "      <td>[1.794106179214274, 1.7925073295393719, 1.7909...</td>\n",
       "      <td>[115, 160, 205, 250, 295, 340, 385, 430, 475, ...</td>\n",
       "      <td>[1.794106179214274, 1.7802836132202329, 1.7676...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...</td>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[120, 125, 130, 135, 140, 145, 150, 155, 160, ...</td>\n",
       "      <td>[1.7925073295393719, 1.7909232748480652, 1.789...</td>\n",
       "      <td>[120, 165, 210, 255, 300, 345, 390, 435, 480, ...</td>\n",
       "      <td>[1.7925073295393719, 1.7788295719024707, 1.766...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...</td>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[125, 130, 135, 140, 145, 150, 155, 160, 165, ...</td>\n",
       "      <td>[1.7909232748480652, 1.7893544285292378, 1.787...</td>\n",
       "      <td>[125, 170, 215, 260, 305, 350, 395, 440, 485, ...</td>\n",
       "      <td>[1.7909232748480652, 1.7773897124807985, 1.765...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cell  Sample                                      History_Cycle  \\\n",
       "0     1       1  [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...   \n",
       "1     1       2  [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...   \n",
       "2     1       3  [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...   \n",
       "3     1       4  [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...   \n",
       "4     1       5  [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...   \n",
       "\n",
       "                                             History  \\\n",
       "0  [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "1  [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "2  [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "3  [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "4  [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "\n",
       "                               Target_Cycle_Expanded  \\\n",
       "0  [105, 110, 115, 120, 125, 130, 135, 140, 145, ...   \n",
       "1  [110, 115, 120, 125, 130, 135, 140, 145, 150, ...   \n",
       "2  [115, 120, 125, 130, 135, 140, 145, 150, 155, ...   \n",
       "3  [120, 125, 130, 135, 140, 145, 150, 155, 160, ...   \n",
       "4  [125, 130, 135, 140, 145, 150, 155, 160, 165, ...   \n",
       "\n",
       "                                     Target_expanded  \\\n",
       "0  [1.7973466099593307, 1.7957194104838883, 1.794...   \n",
       "1  [1.7957194104838883, 1.794106179214274, 1.7925...   \n",
       "2  [1.794106179214274, 1.7925073295393719, 1.7909...   \n",
       "3  [1.7925073295393719, 1.7909232748480652, 1.789...   \n",
       "4  [1.7909232748480652, 1.7893544285292378, 1.787...   \n",
       "\n",
       "                                        Target_Cycle  \\\n",
       "0  [105, 150, 195, 240, 285, 330, 375, 420, 465, ...   \n",
       "1  [110, 155, 200, 245, 290, 335, 380, 425, 470, ...   \n",
       "2  [115, 160, 205, 250, 295, 340, 385, 430, 475, ...   \n",
       "3  [120, 165, 210, 255, 300, 345, 390, 435, 480, ...   \n",
       "4  [125, 170, 215, 260, 305, 350, 395, 440, 485, ...   \n",
       "\n",
       "                                              Target  \n",
       "0  [1.7973466099593307, 1.7832393947563925, 1.770...  \n",
       "1  [1.7957194104838883, 1.7817527911332156, 1.769...  \n",
       "2  [1.794106179214274, 1.7802836132202329, 1.7676...  \n",
       "3  [1.7925073295393719, 1.7788295719024707, 1.766...  \n",
       "4  [1.7909232748480652, 1.7773897124807985, 1.765...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82126933",
   "metadata": {},
   "source": [
    "# INTUITION\n",
    "\n",
    "> Given the sequence `History` over `History_cycle` give me the sequence `Target` over `Target_cycle`\n",
    "\n",
    "Note that for each sample the input sequence grows and the output shrinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "45e5ee25",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_context(\"notebook\",  font_scale=1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a783590",
   "metadata": {},
   "source": [
    "### Plot First Sample of Cell 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "078ae8f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_1_sample_1 = df[[\"History\", \"History_Cycle\", \"Target\",\"Target_Cycle\"]].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "56d70723",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.834012342776987,\n",
       " 1.8321873038112761,\n",
       " 1.8303675518848368,\n",
       " 1.8285535003865527,\n",
       " 1.8267455627053073,\n",
       " 1.8249441522299843,\n",
       " 1.8231496823494675,\n",
       " 1.8213625664526403,\n",
       " 1.8195832179283866,\n",
       " 1.8178120501655899,\n",
       " 1.8160494765531339,\n",
       " 1.8142959104799024,\n",
       " 1.8125517653347787,\n",
       " 1.8108174545066469,\n",
       " 1.8090933913843903,\n",
       " 1.8073799893568927,\n",
       " 1.805677661813038,\n",
       " 1.8039868221417092,\n",
       " 1.8023078837317905,\n",
       " 1.8006412599721653,\n",
       " 1.7989873642517176]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_1_sample_1.History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "741a3841",
   "metadata": {},
   "outputs": [],
   "source": [
    "hist = pd.DataFrame({\n",
    "    \"History\": cell_1_sample_1.History,\n",
    "    \"History_Cycle\": cell_1_sample_1.History_Cycle,\n",
    "})\n",
    "\n",
    "target = pd.DataFrame({\n",
    "    \"Target_Cycle\": cell_1_sample_1.Target_Cycle,\n",
    "    \"Target\": cell_1_sample_1.Target\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7fd75f71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Battery Cycle'), Text(0, 0.5, 'Battery Capacity (Ah)')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "fig.suptitle(\"First Sample of Cell 1\")\n",
    "sns.scatterplot(data=hist, x=\"History_Cycle\", y=\"History\")\n",
    "sns.scatterplot(data=target, x=\"Target_Cycle\", y=\"Target\")\n",
    "ax.legend([\"input sequence\", \"output sequence\"])\n",
    "ax.set(xlabel='Battery Cycle', ylabel='Battery Capacity (Ah)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53aeb5cd",
   "metadata": {},
   "source": [
    "### Plot Last Sample of Cell 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5bdade69",
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_1_last_sample = df[[\"History\", \"History_Cycle\", \"Target\",\"Target_Cycle\"]].iloc[240]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2e16a551",
   "metadata": {},
   "outputs": [],
   "source": [
    "hist = pd.DataFrame({\n",
    "    \"History\": cell_1_last_sample.History,\n",
    "    \"History_Cycle\": cell_1_last_sample.History_Cycle,\n",
    "})\n",
    "\n",
    "target = pd.DataFrame({\n",
    "    \"Target_Cycle\": cell_1_last_sample.Target_Cycle,\n",
    "    \"Target\": cell_1_last_sample.Target\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aafb0d1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Battery Cycle'), Text(0, 0.5, 'Battery Capacity (Ah)')]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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44ov06tWL2traVp3HLtOSJElSJ7Fw4UK22GILNttsM8OwqlpEsNlmmzFkyBAWLlzY6vMYiCVJkqROYuXKlfTt27fS1ZA6jH79+rFy5cpWH28gliRJkjqJt99+m+7dHfUo5XXv3p2333671ccbiCVJkqROxK7S0job+3kwEEuSJEmSqpKBWJIkSZJUlQzEkiRJkipq2rRpdgVXRRiIJUmSJKnA3/72N6ZNm8bixYsrXRW1MwOxJEmSpIo655xzWLFiRaWr0eBvf/sb5557roG4CjhnewdWX5+Y//pyXlmykq3611I3uA81NXYlkSRJUtfSvXt3l5NSRdhC3EHV1yduf3IB/33pAxz704f51NWP8OCzr/HQnNeYu3AZ9fWp0lWUJEmS2kRjY4gjgtNOO40bbriBnXbaiV69erHTTjtx++23N3rsrFmzOPLII+nXrx9bbrklZ555JmvWrGkoN3/+fCKCq6++eoPnjwimTZvWcL4vfvGLAIwaNYqIICKYP39+k/WfPXs2RxxxBEOHDqW2tpYRI0ZwzDHH8OabbzaUqa+v58ILL2SHHXagV69eDBs2jC984QssW7ZsvXOtWrWKL37xi2yxxRb069ePD3/4w7z44ovr1RHghBNOoK6urqT3EuCqq65i9913p3fv3gwZMoTjjz+eV155Zb0ydXV1HH744fz5z39mr732ora2lm233ZZf/vKXG5xv0aJFnHrqqYwcOZJevXoxcuRITjrpJJYuXbpemS984QuMGDGCXr16sf3223PppZc2+T5Wgl/DdFDzX1/O6dc9yso19QwbUMvRe27Dyb/6BwM368lH9xzBO7fsxw7D+jNqiK3GkiRJ6pr+/Oc/8/vf/55TTjmFvn37cumll3LEEUfw/PPPM3jw4PXKHnnkkYwePZoLLriABx98kO9+97ssW7aMH/3oR2U958SJE5kzZw7XXHMNF198MUOGDAFgiy22aLT86tWrOfTQQ+nWrRunn346gwcP5oUXXuCWW25h8eLFDBgwAICTTjqJ3/zmN5x44omcdtppzJ49m8suu4ynnnqKu+++uyHETpo0iWuuuYaPf/zj7L333tx777186EMfKvetW8+5557Leeedx7HHHsvJJ5/Myy+/zCWXXMLf//53Zs6cSe/evRvKzpo1i6OOOopPf/rTnHDCCVx55ZWccMIJ7LHHHuy0004ALF26lPHjxzNr1iwmTZrEuHHjWLBgATfeeCOvv/46/fr1Y/ny5ey///688sorfOYzn2H48OHcd999nHrqqbzxxhtMnTp1o15Tm0kpVfUNqAPSvHnzUkfy12cXppFn3ZJGnnVL+s7tT6ftz7k17f3Nu9MP752dtj/n1jTyrFvSft+5J/35mVfTX59dmOa8ujStXVtf6WpLkiSpHT311FNtfs61a+vTnFeXVvT/lFOnTk1ZNFkHSL169Upz585t2PbYY48lIP3gBz/Y4NiJEyeud/ynPvWpFBENx8+bNy8B6aqrrtrg+YE0derUhscXX3xxKjUj/Otf/0pAeuSRR5osM2PGjASk66+/fr3t1157bQLSbbfdllJK6dFHH01AmjJlynrljjvuuA3qePzxx6eRI0du8FzF7+W8efNSt27d0oUXXrheuYceeihFRPrxj3/csG3kyJEJSH/5y18atr366qupV69e6YwzzmjYds455yQg/fGPf9zg+evrs9+f8847L/Xr12+965dSSp/5zGdSbW1tWrRo0QbHtlZLn4v8tQfqUlEetMt0B7VV/1pqe2SXJwJWrqln4u4juPTe2Ru0Gp9+3WP84dGXuPWJl5nzqt2pJUmSVJriYXr/fekD3P7kgg7z/8lDDz2UUaNGNTzedddd6d+/P3Pnzt2g7Oc+97kNHqeUuOOOO9q1jvkW4JtvvpnVq1c3Wub6669n0KBB7L///rz22msNt/32249u3bpx//33A3DrrbcCMGXKlPWOP+2001pdv+nTp5NSYuLEies993bbbcewYcManjtv1113ZZ999ml4vMUWW7D99tuv957feOON7LHHHhx22GEbPF++pfv6669n//33p1+/fus97yGHHMLKlSt5+OGHW/2a2pJdpjuousF9uOiocZx+3aMA1PaoaQjGQEM4HrhZTz6x98iGoDxycG/O/8gu9O5ZQ89uNby1eq0TckmSJKlRhcP0IPu/5unXPcrYKePZdou+Fa4dbLPNNhtsGzhwIG+88cYG28eMGdPo4+bG/raFUaNGcfrpp3P++edz8cUXs//++3PYYYdx3HHH0a9fPyAbY7xo0aImu10vXLgQgOeee47u3buv9yUAwPbbb9/q+s2ePZv6+nq23XbbZp87r5T3fO7cuRx99NEtPu/jjz/e4muuNANxB1VTE3xgp6GMnTKeRctXMWbLvsxduIzaHjWsXFPfbKvx/970BEfvuU1DYHbMsSRJkhrzypKVDWE4b+Wael5durJDBOJu3bo1uj2l8luwG5toCmDt2rVln6vY9773PT71qU9x0003cccdd3DKKafwjW98g4ceeojhw4dTX1/PsGHDGp2cCmDrrbcu+zlLfT319fV069aN2267rdFjBg4cuN7jtnrP6+vr+cAHPsAZZ5zR6P78eORKMxB3YDU1wbZb9GXbLfqye33i+UXLGTm4D1+d/gTQdKvxSftu22zrcY9uYauxJEmSGobpFYbi2h41bNmvtoK1ap3Zs2fzjne8Y73HACNHjgTWBb/itYWfe+65Dc7VVNhszs4778zOO+/M2WefzcMPP8zee+/NT37yE84//3xGjx7Nfffdx/jx4+nVq1eT5xg5ciRvv/028+bNY/To0Q3bn3nmmQ3KDhw4sNF1kotfz+jRo1m7di1jxoxpdFbq1hg9ejT//ve/WyyzYsUKDjrooDZ5zvbiGOJOoqYmqBvSl8PHDefWKeM54J1D+PYRu9It2GCssWOOJUmSVIr8ML38/ydre9Rw0VHjqBvcp8I1K98Pf/jDDR5HBIceeigA/fv3Z8iQIcyYMWO9co3NQt2nT/b6GwucxZYsWcLbb7+93radd96Z7t27s3LlSiCbAXv16tVccMEFGxy/atUqlixZAsAHP/hBgA2WJvr+97+/wXGjR4/mzTff5PHHH2/Y9vLLLzN9+vT1yk2YMIGamhrOO++8Dc5RX1/PokWLWnyNxSZMmMDMmTO5+eabN9iXb0k+8sgjmTFjxgZjlAFee+21VrXytwdbiDuZUlqN8/fljDm21ViSJKn6FA7Te3XpSrbs13n/Pzh79mwmTJjAwQcfzAMPPMC1117L5MmT1xs7O2nSJC644AImTZrEnnvuyYwZM5g1a9YG59pjjz0AOPvssznmmGPo0aMHhx12WENQLnTvvffy+c9/niOPPJLtt9+etWvXcs011xARHHHEEQC8733vY9KkSUybNo2ZM2dy4IEHUlNTw6xZs7juuuv49a9/zUEHHcS4ceM49thjufTSS3njjTfYe++9ueeee3j22Wc3eN5jjjmGs846iwkTJjBlyhTeeustfvzjH/POd76Tf/7znw3ltttuO8477zzOOecc5syZ0/A65syZww033MDZZ5/NpEmTynqvv/SlL/H73/+eiRMnNiy7tHDhQm688UZuvPFG6urqOPPMM7nppps49NBDOfHEExk3bhxLlizhscce44YbbmDp0qV07175OFr5GqjV8q3G2wzqw7h3bN4w1viiu55hyvvHsOrttSWNOS5e33inrftTn+DVpSsNyZIkSV1cYYNLZ3b99dfzla98hbPOOova2lrOOOMMvvWtb61X5mtf+xoLFy7k+uuv57rrruODH/wgt912G1tuueV65XbbbTe++c1v8sMf/pDbb7+d+vp65s2b12ggfte73sWhhx7KLbfcwuWXX85mm23Gu971Lm677Tb23nvvhnJXXHEFe+yxB1dccQVf/vKX6dWrF6NGjeLTn/4048aNayj385//nC222IJf//rX3Hjjjbz//e/nT3/603rdwQEGDx7M9OnTOf300znzzDMZNWoU3/rWt5g9e/Z6gRiyYD9mzBi+//3vM3XqVGpqathmm204/PDDOfjgg8t+r/v378+DDz7I1772Nf7whz/w85//nKFDh3LIIYc0rNvcp08fZsyYwTe+8Q2uv/56rrzySgYOHMjYsWP59re/3eRY5U0tOkpTdaVERB0wb968eW3Wp76S6usT819fzqLlq6jt0Y1Zryzjq9OfYNL4bfnZA3OZNH5bLrs3+4bpc+/bjisfnLteq/HAzXryyfeM5JJ7nJBLkiSpo/nPf/7DDjvsUOlqdCjTpk3j3HPP5Y033mDzzTevdHXaTUQwdepUpk2bVumqdDgtfS7mz5+fn7l7VEppfuE+W4i7mOJv+HYcNmC91uOWZqqeuPuIhjBs12pJkiRJXZmBuIsrd6bqcrpW22osSZIkqTOraCCOiGHAqcC7gT2BvsD7Ukr3l3j8UcAZwFhgNfAE8M2U0t3tUuFOrqkxx4WtxlDahFwGY0mSJEmdXaVbiLcHzgKeBR4H9in1wIj4HHAZ8CfgKqA3cCJwZ0QcmlK6q+2r2zU012p8w8wXOfXAMaxc0/SEXE11p+7ds4ae3Wp4a/Vau1VLkiRpk5g2bVpVjKut9rmf2kulA/FMYEhK6fWIOByY3kL5Qp8H/g4clnK/HRHxC2AB8HHAQFyC4lbjV5euZGj/WlKiya7VjXWn/t+bnuDoPbdxzLEkSZKkTqOigTiltHQjDu8PzEnrf1WyGFiRu6kMjU23P3Jw412rG+tOfdK+2zrmWJIkSVKnUukW4o3xZ+CoiPgCcDNQSzaeOIAfNnZARGwObF60eUT7VbFza6pr9XOvL9+gO3VLY47tWi1JkiSpo+nMgfg0YEvg0twN4BWySbmeaOaYqe1esy6osGt1YzNV5++bWs6psa7Vth5LkiRJqqTOHIiXA08DzwO3Av2ALwJ/jIjxKaW5jRzzfeDqom0jgAfar5pdS1MzVV901zNMef8YLr13NtD0TNX5rtWucyxJkiSp0jpzIL4eWJlSmpDfEBE3AbOB84GPFR+QUlpMNs6YgmPatZJdVXF36l2GD2DR8lX87uS9Wb22foPlnIq7VrvOsSRJkqRK65SBOCK2BT5AtsxSg5TSooh4EHhvRSpWpRqbkKu+aDknWL9rtWOOJUmSJFVapwzEwFa5+26N7OtB531dXUZLXatXvd30Oscu5yRJkiRpU+gUwTEiRgOklObkNj0L1ANHAz8rKDcCGA/cv4mrqCY01bW6tke3Jtc5hvKWc9pp6/7UJ3h16UpDsiRJkgC44IILGDt2LIcffni7P9ff/vY3br/9dk477TQ233zzdn8+tZ2KB+KIOCf34w65+09ExL7A4pTSZblt9+Tu6wBSSgsj4ufApIi4B7iRbF3iU8iWX7pgU9Rd5SnuWr3jsAGNrnNcznJOAzfrySffM5JL7nHWakmSJK1zwQUXcPjhh2+yQHzuuedywgknGIg7mYoHYrIJsArlxwU/B1xG0z4LPAacxLoA/Ajw8ZTSX9q0hmoXTa1zXM5yThN3H9EQhgtDssFYkiRJUktqKl2BlFI0casrKFNX+Di37e2U0mUppd1SSv1ytwNTSn/e1K9BGy8/5vjwccO5dcp4DnjnEL59xK7c/NhLTHn/mCYn5CoOyflgfMWMuXz+t//ixF88woPPvsbf57/OYy+8wUNzXmPuwmXU16dKvlxJkiQV+ec//8mhhx5Kv3796NevHx/4wAd47LHH1itzwgknUFdXt8Gx06ZNW2/1mIjgzTff5Be/+AURQURwwgknrFd21qxZHHnkkfTr148tt9ySM888kzVr1jScY/78+UQEV1999QbPFxFMmzat4Xxf/OIXARg1alTD882fP7/J1zp79myOOOIIhg4dSm1tLSNGjOCYY47hzTffbChTX1/PhRdeyA477ECvXr0YNmwYX/jCF1i2bNl651q1ahVf/OIX2WKLLejXrx8f/vCHefHFF9erYznvXd5VV13F7rvvTu/evRkyZAjHH388r7zyynpl6urqOPzww/nzn//MXnvtRW1tLdtuuy2//OUvNzjfokWLOPXUUxk5ciS9evVi5MiRnHTSSSxdunS9Ml/4whcYMWIEvXr1Yvvtt+fSSy9t8n1sCx2hhVhqUO5yTrB+SHZiLkmSpM7nySefZL/99mPgwIF85StfAeDHP/4x48eP5+GHH2aHHXZo4Qzr+9WvfsXkyZPZY489OPnkkwEYPXr0emWOPPJIRo8ezQUXXMCDDz7Id7/7XZYtW8aPfvSjsp5r4sSJzJkzh2uuuYaLL76YIUOGALDFFls0Wn716tUceuihdOvWjdNPP53BgwfzwgsvcMstt7B48WIGDBgAwEknncRvfvMbTjzxRE477TRmz57NZZddxlNPPcXdd9/dEGInTZrENddcw8c//nH23ntv7r33Xj70oQ+V9RqKnXvuuZx33nkce+yxnHzyybz88stccskl/P3vf2fmzJn07t27oeysWbM46qij+PSnP80JJ5zAlVdeyQknnMAee+zBTjvtBMDSpUsZP348s2bNYtKkSYwbN44FCxZw44038vrrr9OvXz+WL1/O/vvvzyuvvMJnPvMZhg8fzn333cepp57KG2+8wdSpUzfqNTXFQKwOq5TlnG6Y+SKnHjiGlWvWOjGXJElSa9TXw6I5sHQB9BsKg0ZDzabtSHrOOeewdu1aHnzwQUaOHAnAcccdx9ixYznnnHO44YYbyjrfxz/+cT7/+c+z7bbb8vGPf7zRMmPGjGk47+c+9zl69+7NT37yE770pS8xatSokp9r1113ZY899uCaa67h8MMPb7QVttBTTz3FvHnzeOSRR9hrr70athe25j7wwANcffXVXH/99RxxxBEN2/faay+OOeYY7rjjjoYW9GuuuYYpU6ZwySWXNLyWj33sYzz++OMlv4ZC8+fP5/zzz+c73/kOZ5xxRsP2D37wg+yzzz784he/4DOf+UzD9v/85z/85S9/YZ999gHgqKOO4h3veAdXXXUVF154IQDf+c53eOqpp/jjH//IYYcd1nDs1KlTSSnruXnRRRfx3HPP8dhjjzW8/5MnT2bgwIFccMEFTJkyhYEDB7bqNTWn4l2mpXIUd62++Oh38cGdh3L4uOF8c8IudIt1Y49LmZjrihlz+cat/+H2JxfwoR88wOnXPcYfHn2JW594mTmv2rVakiR1cfX18PTNcPl4+MX/ZPdP35xt30TWrl3LnXfeycSJExvCMGTdcSdMmMAdd9zB2rVr2/x5P/e5z23wOKXEHXfc0ebPVSjfAnzzzTezevXqRstcf/31DBo0iP3335/XXnut4bbffvvRrVs37r//fgBuvfVWAKZMmbLe8aeddlqr6zd9+nRSSkycOHG9595uu+0YNmxYw3Pn7brrrg1hGLKW8e233565c+c2bLvxxhvZY4891gvDefmW7uuvv57999+ffv36rfe8hxxyCCtXruThhx9u9Wtqji3E6pQaaz0eObhPu0zMZeuxJEnqshbNgemTYc2K7PGaFdnjyTvCkDGbpAoLFy7krbfeYvvtt99g39ixY7n22mt57bXX2Gqrrdr0eceMGdPo4+bG/raFUaNGcfrpp3P++edz8cUXs//++3PYYYdx3HHH0a9fPyAbY7xo0aImu10vXLgQgOeee47u3btv0KLd2HtZqtmzZ1NfX8+2227b7HPnbbPNNhuUGThwIG+88UbD47lz53L00Ue3+LyPP/54i6+5rRmI1WXkW4+3GdRnveWcLrrrGaa8fwyX3jsbKH1iruaWdTIkS5KkLmHpgnVhOG/NCli2YJMF4nI0NvkT0C4tyO35XN/73vf41Kc+xU033cQdd9zBKaecwje+8Q0eeughhg8fTn19PcOGDWt0ciqArbfeuuznLPX11NfX061bN2677bZGjynuttytW7dGz5vvCl2q+vp6PvCBD6zXTbtQfjxyWzMQq8tpy4m5mmo9du1jSZLUJfQbCj16rx+Ke/SGvkM3WRW22GILNttsM5555pkN9j3zzDP06dOnYaKqgQMHsnjx4g3KPffccxtsayoA5s2ePZt3vOMd6z0GGrpt54Nf8fO15rkas/POO7Pzzjtz9tln8/DDD7P33nvzk5/8hPPPP5/Ro0dz3333MX78eHr16tXkOUaOHMnbb7/NvHnz1ps0rLH3stT3bvTo0axdu5YxY8a0OB66VKNHj+bf//53i2VWrFjBQQcd1CbPWSrHEKtLy4fjPesG8653DGSvusEctuvWTNgtG3Nc26OmYWKu/Pjj5lqPGwvJLvEkSZI6rUGjYcLlWQiG7H7C5dn2TaRbt24ccsgh3HjjjTz//PMN259//nmmT5/eMCMzZKHpzTffXG/CqJdffpnp06dvcN4+ffo0GgDzfvjDH27wOCI49NBDAejfvz9DhgxhxowZ65VrbBbqPn36ABuG58YsWbKEt99+e71tO++8M927d2flypVANgP26tWrueCCCzY4ftWqVSxZsgTIJroCNlia6Pvf//4Gx5X63k2YMIGamhrOO++8Dc5RX1/PokWLWnyNxSZMmMDMmTO5+eabN9iXb0k+8sgjmTFjxgZjlAFee+21slucS2ULsapOcdfqV5euZGj/WlLKxiE/9/ryJluPofGQ7BJPkiSpU6qpgbGHZWOGly3IWoYrMMv017/+de666y723XdfPvvZzwLZskvdu3fn61//ekO5Y445hrPOOosJEyYwZcoU3nrrLX784x/zzne+k3/+85/rnXOPPfbg7rvv5qKLLmLrrbdm1KhRvPvd727YP3v2bCZMmMDBBx/MAw88wLXXXsvkyZPXGzs7adIkLrjgAiZNmsSee+7JjBkzmDVr1gb132OPPQA4++yzOeaYY+jRoweHHXZYQ1AudO+99/L5z3+eI488ku233561a9dyzTXXEBENM0q/733vY9KkSUybNo2ZM2dy4IEHUlNTw6xZs7juuuv49a9/zUEHHcS4ceM49thjufTSS3njjTfYe++9ueeee3j22Wc3eN5S37vtttuO8847j3POOYc5c+Y0vI45c+Zwww03cPbZZzNp0qSSrmvel770JX7/+98zceLEhmWXFi5cyI033siNN95IXV0dZ555JjfddBOHHnooJ554IuPGjWPJkiU89thj3HDDDSxdupTu3dshvqaUqvoG1AFp3rx5SVq7tj7NW7g03TDzhbT9Obemvb95d/rRfbPTRXc+nbY/59Y08qxb0nfvyH7+7h1Pp5Fn3ZJGnnVL+s7t2bb8/cizbkl7f/Pu9MN7Zzec53t3Pp3u+PfL6dHnFqW/PrswzXl1aVq7tr7SL1mSJHUiTz31VKWr0G5mzpyZDj744NSnT5/Up0+fdMghh6R//etfG5S78847084775x69uyZtt9++3TNNdekqVOnpizarDNr1qy0//77p969eycgHX/88Sml1FD2mWeeSRMnTkx9+/ZNQ4YMSWeccUZavXr1eud466230kknnZQGDBiQ+vXrl4466qj06quvJiBNnTp1vbLf/OY30/Dhw1NNTU1qLl/MnTs3nXjiiWn06NGptrY2DRo0KL3vfe9Ld99993rl6uvr049//OO02267pdra2jRgwIA0bty4dPbZZ6eFCxc2lFuxYkWaMmVKGjx4cOrTp0867LDD0gsvvNBoHUt971JK6Xe/+116z3vekzbbbLPUt2/ftOOOO6bTTjstzZ8/v6HMyJEj00c+8pENjt1///3T/vvvv962hQsXps9+9rNp2LBhqWfPnmmbbbZJkyZNSkuXLm0o8+abb6YzzzwzbbvttqlHjx5pyy23TPvtt1+65JJLUn190/9vbulzMW/evAQkoC4V5cFI7dT03FlERB0wb968eW3WR16dX319Yv7ry9drPf7XC4v56vQnGsYQr1yzlstnzGXlmno+//7tuOzeZxvuAT73vu248sG5DV2rf/eP5xttPe7ds4ae3Wp4a/VaW5ElSVKz/vOf/7DDDjtUuhqd2rRp0zj33HN544032HzzzStdnXYTEUydOnW99Y27qpY+F/Pnz8/PxD0qpTS/cJ9dpqVGNLWsU2NdrEtd4umkfbe1i7UkSZLUgRiIpRI1F5JLWeKpcBxycyH55F/9wyWeJEmSpE3AQCxthHKWeIINW48bC8ktLfG089YD2KpfL95aYxdrSZIkaWMYiKU20lgLcn194vlFyxk5uA+X3DNrg9bj/H1jXawbW+Lpd/94ntru3Zhy7b/sYi1JktQK06ZNq4pxtdU+V1SpDMRSOype4qm49bi5Ltaw4RJPdrGWJEmS2o6BWNoEmmo9bq6LdWPrILemi7UhWZKkriWlRIT/jkuw8S3hBmKpQlrqYv3V6U9ww8wXOfXAMaxcs7ZVXawNyZIkdS3du3fn7bffpkePHpWuitQhvP3223Tv3vpYayCWOpDiLtbFSzw1Ng65uS7W5YbkHYb1Z9QQg7EkSR1VbW0ty5YtY+DAgZWuitQhLF26lNra2lYfbyCWOqCWlngqtYs1lBeSbT2WJKlj22KLLXj++efp1asXvXv3tuu0qlZKiRUrVvDaa6+xzTbbtPo8BmKpk2hNF+tSQ7JdrCVJ6hxqa2vZaqutWLBgAatWrap0daSK6tWrF1tttZUtxFK1aqmLdakh2XHIkiR1HgMGDGDAgAGVrobUJRiIpS6guS7WpYRkxyFLkiSpGhmIpS6qnJD83OvLHYcsSZKkqmMglqpIUyHZcciSJEmqRgZiqco5DlmSJEnVykAsCXAcsiRJkqqPgVhSkyoxDnnlmnpGDu7N+R/Zhd49a+jZrYa3Vq+19ViSJEltzkAsqSztOQ555Zp6hg2o5eg9t+F/b3qCo/fcxsm6JEmS1G4MxJI2WluNQwYawvFJ+25b8mRddrGWJElSaxiIJbWZjR2HvHJNfUM4tou1JEmS2puBWFK7KjckQ9ZqnL9vTRdrQ7IkSZJKYSCWtMk1F5IXLV/FmC37ctFdzzDl/WNY9XZ5XaxLGYdsF2tJkiSBgVhSB1EYknevT+wyfACLlq+itke3srpYQ/PjkG09liRJUp6BWFKHU9yCvOOwASV3sW5pHLJdrCVJkpRnIJbU4ZXTxfrSe2cDTY9DLrWLdWFI7tEtDMeSJEldkIFYUqfUVBfr3528N6vX1jc5DrmULtaFIfnkX/3DdZAlSZK6KAOxpE6vsRbk+ibGIUPLXaxhXUh2HWRJkqSuy0AsqUtqahxyKV2sy1kH2dZjSZKkzstALKkqlNPFujAkN7cOsq3HkiRJnZuBWFLVaamLdWFInrtwWZPrILfUeuzs1ZIkSR2bgViSaDokP79oeZPrIDfXeuwST5IkSR2fgViSmlBTE9QN6cs2g/o0ug7yc68vb7L1GEpf4sku1pIkSZVhIJakFjS1DnJzrcelLvFkF2tJkqTKMRBLUiu01Hpc6hJP5XSx7tEtDMeSJEltyEAsSRuhqdbjUpd4KrWL9cm/+odLPEmSJLUxA7EktbFylnha9XZpXaxd4kmSJKntGYglqR21tMRTbY9uJXWxdoknSZKktmcglqRNrDgk7zhsQItdrFuzxJOtx5IkSc0zEEtShZXSxXruwmVlLfFk67EkSVLLDMSS1IE01cW63CWeypm92pAsSZKqlYFYkjq41izxVOrs1S7xJEmSqpmBWJI6iXKWeCp19uqWlnjaeesBbNWvF2+tsfVYkiR1PQZiSerEmhp/XOrs1c0t8fS7fzxPbfduTLn2X3axliRJXZKBWJK6iNbMXp2/b2z8sV2sJUlSV2cglqQuqpTZq5tb4qk1XazfuWU/dtq6P/UJXl260pAsSZI6NAOxJFWBpmavbm6JJyivi3X+50++ZySX3DPbkCxJkjo8A7EkVamWlni65J5ZZXWxzv+cD8PNhWQn65IkSR2BgViS1KB4iadyulgDJYVkJ+uSJEkdhYFYkrSB1nSxzofilkJyOZN1GZIlSVJ7MhBLkkrSUhfrr05/ghtmvsipB45h5Zq1TYbkUifrckZrSZLU3gzEkqRWK+5i/erSlQztX0tKNBmSobTJulqa0dpxyJIkaWMZiCVJG62x1uORgxsPyaVO1tXcjNaOQ5YkSW3BQCxJahfNheRSJuvK3zc2o7XjkCVJUlswEEuSNplyJutqbkbrjR2HbEiWJElgIJYkVVhrZrSG1o9DNiRLkqQ8A7EkqcNpaUbrjRmH3FJIzk/a9c4t+7HT1v2pT/Dq0pWGZEmSuiADsSSpUyie0bq145CbC8n5SbvyP3/yPSO55B5DsiRJXZWBWJLUqWzsOOT8fWMhOR+Q8z/nw7AhWZKkrslALEnq9NoqJBe2Hm9sSN5hWH9GDTEYS5LUkRmIJUldUmtC8qq31za0HsPGhWRbjyVJ6vgMxJKkqtFSSK7t0Y2Rg/vw1elPcMPMFzn1wDGsXFNeSLaLtSRJnYeBWJJU1YpD8o7DBjDuHZvz6tKVDO1fS0qUFZIdhyxJUudhIJYkqUBjrcgjB/cpOSS35ThkQ7IkSe3LQCxJUgvKCcnPvb68zcYhF4fknbcewFb9evHWmrUGZEmS2oCBWJKkVmgqJD+/aPlGj0NuLCT/7h/PU9u9G1Ou/Rcr19QzcnBvzv/ILvTuWUPPbjW8tdqQLElSuQzEkiS1kZqaoG5IX7YZVHoX61JD8kn7btsQlocNqOXoPbfhf296gqP33KZhuyFZkqTyGIglSWpjGzsOubGQXBiWDcmSJLUNA7EkSZvAxoZkoCEsb0xILpy0a4dh/Rk1xGAsSapeBmJJkiqknJB8yT2zmPL+MVx672yAVoXkwkm7bD2WJMlALElSh9JcSF60fBW/O3lvVq+tZ8yWfbnormfKCsmFk3bZxVqSJAOxJEkdXmMhub4+scvwAWWFZMchS5K0vooG4ogYBpwKvBvYE+gLvC+ldH+Jx9cApwAnA2OAZcBM4HMppTntUWdJkjqC1oTkVW+vbZNxyIZkSVJXUekW4u2Bs4BngceBfco8/pfABOBK4BJgAPBfwCDAQCxJqiotheTaHt0aJu2C1o1DdrIuSVJXUulAPBMYklJ6PSIOB6aXemBEHAscBYxPKT3cTvWTJKlTKw7JOw4b0DAeuTXjkMHJuiRJXUdFA3FKaelGHH4aMD2l9HBEdAd6ppTeapuaSZLUNRUG5N1bMQ55Yyfr6tEtDMeSpA6j0i3ErRIR/YG9gD9GxOXA8UCviHgSOD2ldGcTx20ObF60eUQ7VlWSpA5rU0/WdfKv/rFe1+qdtu5PfYJXl640JEuSKqJTBmJgNBDAF4E3gM8Ca4EzgT9FxD4ppb83ctxpwNRNVUlJkjqb9pysq7Br9cDNevLJ94zkknscfyxJqpzOGojz/0r3A3ZLKb0AEBG3A3OBr5JNtlXs+8DVRdtGAA+0Sy0lSeoC2mqyrsKu1RN3H9EQhgtDssFYkrQpddZAvCJ3/5d8GAZIKb0aEXcB723soJTSYmBx4bYI/6GVJKlcrZmsq7DVuDgkOzGXJKkSOmsgfjl3/0oj+14BBm7CukiSVPVKmaxr7sJlDa3GsH5IdmIuSVIldMpAnFJ6KSJeAYY3snsEsHATV0mSJOU01cX6+UXLG7pW3zDzRU49cAwr16zdqIm57FotSdoYnSIQR8RogJTSnILNvwc+GxFjU0pP58rVAQcDv9vklZQkSU2qqQnqhvRlm0F9GPeOzXl16UqG9q8lJRg5uA/Pvb687Im5bDWWJG2sigfiiDgn9+MOuftPRMS+wOKU0mW5bffk7usKDv0m8FHgvoi4FHgb+AKwEjivXSstSZJapbHW45GD+6zXegylTcxlq7EkaWNVPBAD5xc9PjF3/xxwGU1IKb0cEeOB7wFfAWqAB4EvpZSebY+KSpKktlfcelzqxFy2GkuSNlbFA3FKqcV/oVJKdU1snw18uK3rJEmSNr1yJ+ay1ViStLEqHoglSZKKlTIxF5TWamwwliQ1xUAsSZI6haa6VjfXamx3aklScwzEkiSpUynuWt1cq7HdqSVJzTEQS5KkTqulVmMn4ZIkNcdALEmSOr2mWo0bW9/YVmNJUp6BWJIkdSmFrcatmYTLVmNJqh4GYkmS1CW1ZhIuW40lqboYiCVJUpdWziRc4NJNklRNyg7EETEQ2BJIwMKU0httXitJkqR24NJNkqRCLQbiiKgBPgJ8FNgfGFq0fwFwP/B74I8ppfq2r6YkSVLbaYulm1auqae2Rw0XHTWOD+w01FAsSZ1Qk4E4IroBnwW+DGwNLAf+DtwKvA4EMAjYDvgwcCzwckR8E/hJSmlt+1ZdkiRp47Vm6aZ8OJ64+wieXrCE4Zv3ZpfhAwzFktTJNNdC/BQwArgW+BUwo6nW31wr8gHAJ4DvAJ8HdmjTmkqSJLWjcpZuGjagdr1u1Dc9+pLdqCWpE2ouEP8J+HZK6ZWWTpILyvcC90bEV4Cz2qh+kiRJm1xLSzc5K7UkdQ1NBuKU0umtOWFKaQHwxVbXSJIkqYNoqjv1nIXLXMtYkroAl12SJElqQXF36ideepMrZswtaS1jJ9+SpI6rNcsubQbUAYPJJtZaT0ppxsZXS5IkqWOqqQl2GT6Ai44ax+nXPQo0vZaxk29JUsdWciDOBeGLgE81cVyQrU3crW2qJkmS1DHV1AQf2GkoY6eMb3YtYyffkqSOrZwW4kuAk8iWXbqXbOklSZKkqlTKWsZOviVJHVs5gXgC8NuU0sfaqzKSJEmdUWsn3zIYS1JllROIa4H726kekiRJnV45k28Vz0rtxFuStOnVlFH2H8CY9qqIJElSV1I4+VZtj+y/XIWTbxV2pwYYuFlPnl6whPtnvcrchcuor0+VrL4kVYVyWoi/DNwcEdellP7RXhWSJEnqKpqbfKtwVurCybfsRi1Jm06TgTgift7I5heBv0XEQ8BcYG3R/pRSOqkN6ydJktSpNTX51nOvL2+YlbqpbtQjB/d2VmpJakfNtRCf0My+9+ZuxRLZTNSSJEkqUjj5VuGs1I11oy6cldoxxpLUPpoMxCmlcsYXS5IkqUTFs1IvXLaKnz0wd71u1MXheOLuI3h6wRKGb96bXYYPMBRLUhsoZwxxiyKiV0ppVVueU5IkqavKd6euG9yHi44axzMLljR0o86H48LxxSvX1HPToy/ZjVqS2kibBOKI2IOsq/TRwOC2OKckSVK1yE++teOwfg3dqCGblbqpbtROviVJG6/VgTgiBgEfB04EdgECmNVG9ZIkSaoqxd2o87NSz1m4bINu1K5hLElto+xxwhFxaET8DngJuBjoBZwL7JJSGtvG9ZMkSaoq+W7Ue9YN5rBdt+bAsVs1rGPc1ORbJ+27LU8vWMITL73p+sWSVIaSWogjoo6sJfh4YATwGnA9cBxwdkrpxvaqoCRJUrWqqQl2GT6Ai44ax+nXPQqw3hrGji+WpI3TbCCOiI+RBeH9ydYcvgX4AnArMBL4WHtXUJIkqZrlxxePnTK+oRv13IXLHF8sSW2gpRbiXwFzgdOA36aUXs/viPCPqiRJ0qaQ70a97RZ92b0+NaxhPP/15Y4vlqSN0NIY4lVAHfAR4AMR0bvdayRJkqQm5SffOnzc8BbHFwMM3KwnTy9Ywv2zXmXuwmWOMZakAi0F4mFkrcODyVqLF0TElRGxH9ms0pIkSaqAwvHF+VBcOL4YaBhjfMWMuZw9/d/84dGXuPWJl5nzqsFYkqCFLtMppcXAZcBlEbE72VrDxwInAAuBBAxo3ypKkiSpMc2NLy5sLS7uRj1ycG8n35IkyliHOKX0T+CfEXE6cARZOD4A+FlEnEo26/T0lNKT7VFRSZIkbaip8cVfnf5Ek8s05SffcoyxpGpX9jrEKaVVKaXfpJQOBEYD3wAGAucBj7Vx/SRJklSiwvHFt04Zz/gxQzboRu0axpK0TsktxI1JKc0HvhYRU4FDyZZokiRJUgXlW43rBvfhoqPG8cyCJQ3dqBtbw3jgZj0BeGHRWy7TJKmqbFQgzkspJeD23E2SJEkdQH6M8Y7D+jV0owbWW8PYZZokVbMmA3FEHJhSuqc1J42Ig1JKd7e+WpIkSWoL+W7U2wzqw7h3bN4w+dachcuaXaaptkcNdYP7OOGWpC6tuRbi2yPiAeAi4LaU0trmThQRPYD/IVum6T1Az7aqpCRJkjZO8eRbT7z0JlfMmNvoMk35luOP7jmCd27Zz27Ukrqs5gLxbmRh+I/Awoi4G3gEmAMsIluHeBAwBtgbOBDYHLgTGNduNZYkSdJGKVzDuHB8sd2oJVWbJgNxSunfwCER8R7gFOAjZGsQF08/GMAS4Ebgxymlv7dTXSVJktRGGhtf3NgyTWA3akldV4uTaqWUHgIeiohuwB7AjsAWZMF4IfBv4F8ppfr2rKgkSZLaVvH44oXLVvGzB+xGLal6lDzLdG4M8SO5myRJkrqI5pZpaqob9cjBvTn/I7vQo1uwVf9aW40ldUptsuySJEmSOr9Su1EPG1DL0Xtuw8m/+odjjCV1agZiSZIkNSilG3VxOJ64+wieXrCE4Zv3ZpfhAwzFkjqNmkpXQJIkSR1Pvhv1XiMHcdFR4+gWUNsj+69jPhznxxdf+eBcLr3nWY6+4iFuf3IB9fXFc7BKUsdkC7EkSZKa1Fg3asjCsbNRS+rsDMSSJElqVnE36kXLVzFmy77MWbjM2agldWoGYkmSJJUk34162y36snt94omX3uSKGXObnY3aCbckdWQljyGOiHMiYlh7VkaSJEmdQ01NsMvwAVx01Dhqe9Q0ORv1Sftuy9MLlvDES286tlhSh1POpFrnAc9HxM0RcXhEdGuvSkmSJKnjy48vvnXKeMaPGbJeMC6ecGvKtf/kwWdf46E5rzF34TLDsaQOoZwu0+8GTgKOAf4beDUifgH8PKU0qz0qJ0mSpI4t3426bnAfLjpqHM/kJtVy3WJJnUHJLcQppb+nlD4DDAM+BcwCzgT+ExEzIuITEdG7neopSZKkDizfWjxht+F8c8IudKtpet1iu1FL6ijKXoc4pbQipfTLlNL+wPbAd4DRwNXAyxHxo4gY16a1lCRJUoeXn4368HHDOXDsVi2uW2w3akmVVnYgLjIPmAn8BwigL/BpYGZE/MlJuCRJkqpP8YRbsOG6xYXdqI/96cP896UPcPuTCwzFkjapVi27FBE7kY0n/jgwGHgZ+DrwM2A1cArw/4CfAx9sk5pKkiSp08h3oR47ZXyj6xYXh+OJu4/g6QVLGL55b3YZPsCxxZI2iZIDcUT0BY4lC8J7AfXA7cAVwJ9SSvUFxb8WEcuAqW1YV0mSJHUiza1bXNyNOh+Or5gx1wm3JG0y5XSZfgX4CdmkWucBo1JKh6WUbi4Kw3nPAU6yJUmSpJK6UQMM3KwnTy9Ywv2zXnVcsaR2V06X6buAnwK3NRGA15NS+h3wu9ZWTJIkSV1LS92oi1uLXZ5JUnsrp4X4IuCRpsJwRAyJiP3aplqSJEnqivLdqPesG8xhu2693mzULs8kaVMrJxDfBxzczP4Dc2UkSZKkFhV3o25seabf/+NF7nn6FW594mXmvGoXakltq5wu0y31U+lGNtGWJEmSVJLCbtQLl63iZw/MbWgpHrhZT7tQS2pX5S671NxXcvsAr21EXSRJklSF8t2o6wb34aKjxvH0giWsXFPf5IRbtT1qqBvch7rBfQzGkjZKs4E4Ik4FTi3Y9P2I+EYjRQcC/cnWHZYkSZLKlm8tHr55b66YMbehCzU44Zak9tFSC/FisuWTAOqA18mWXyqUgH8DfwMubsO6SZIkqcoUjit+JtcaXNxaPGxALRN3H8HTC5YwfPPe7DJ8gKFYUqs0G4hTSr8AfgEQEfOAL6eU/rgpKiZJkqTqlG8p3nFYP0YO7sNXpz+xwYRb+XB8xYy5thRLarWSxxCnlEa1Z0UkSZKkvJqaoG5IX7YZ1Idx79h8gwm3HFcsqS2UO6mWJEmStMk0NeEWOK5Y0sZrMhDnukjXA2NTSmsiYm4J50sppdFtVjtJkiSJDSfcclyxpLbQXAvxc2QTZuWXWnqe5pddkiRJktpN4YRbp1/3qOOKJW20JgNxSumA5h5LkiRJm1q+pXjslPGOK5a00RxDLEmSpE7FccWS2kpNqQUjYreI+Fwz+z8XEePapFaSJElSC/KtxQeO3YraHtl/a4vHFZ+077Y8vWAJT7z0JvX1jv6TtL6SAzEwFfhQM/s/CHxt46ojSZIkla5wXHFtj5oNxhVf+eBcLr3nWY6+4iFuf3KBoVjSesoJxHsBf25m/5+B/9q46kiSJEnlybcU3zplPOPHDKG2R02T44rvn/UqcxcuMxhLAsobQzwEWNTM/sW5MpIkSdIm5bhiSa1RTgvxq8BOzezfmeYDsyRJktSuHFcsqRzlBOK7gUkRsUEojogdgZNyZSRJkqSKcVyxpFKV02X668BE4O8R8XPg0dz2ccCJwGrg/LasnCRJktQaLa1XPGxALRN3H8HTC5YwfPPe7DJ8gN2npSpUcgtxSmkOcCAwFzgFuCJ3OwWYAxyUUppdzpNHxLCIuCAi7ouIpRGRIuKAcs6RO0+3iHg8d/xp5R4vSZKkric/rnivkYO46KhxdKuxpVjS+sppISal9A9g59x6w2Nym2ellB5r5fNvD5wFPAs8DuzTyvN8Bti2lcdKkiSpC8u3Fg/fvDdXzJjb5AzUtT1qqBvch7rBfWwtlqpEWYE4L6X0KOu6TG+MmcCQlNLrEXE4ML3cE0TEIOA84DvAuW1QJ0mSJHUxheOKnYFaUl45k2q1uZTS0pTS6xt5mvPJunH/qg2qJEmSpC7KGaglFSsrEEfEeyPilohYGBFvR8Taotvb7VXRJuqzCzAZOB3wL5YkSZKa5QzUkgqVHIgjYj/gPuDdwMO5Y+8D/g4E8G82fSvtpcAfUkoPlFI4IjaPiLrCGzCiXWsoSZKkDiXfUnzrlPGMHzOE2h41G4wrXrmmntOve5T5ry+vcG0ltadyxhCfDbwM7EnWGvsq8M2U0r0RcQhwPdmM05tERBxJNgnXDmUcdhowtV0qJEmSpE4jPwN13eA+G4wrBhqWZZr1ylIAJ9qSuqhyukz/F/CzlNJCIP/XogYgpXQnWevwJlmHOCJqgQuBS1JKc8s49PvAqKLb+DavoCRJkjqFxsYVF3af/sw1/+S/L33A7tNSF1VOIO4FvJT7eVXuvl/B/keBPdqgTqU4BRgM/LqRrs+Dctt6FB+UUlqcUppfeANe3ER1liRJUgdUPK7Yibak6lFOIH6ZXOhMKS0HFgM7F+wfAWyqSbW2AfqShfB5uVt+HPH/5h6P3kR1kSRJUidXOK54l+H9nWhLqhLljCH+O/Degsd3Al+MiOfIgvXnySbbanMRMRogpTQnt+lK4P6iYlsClwM/B27Gll9JkiSVIT+uGGh2oq2xU8Y3lJPUuZUTiK8EToiI3imlFcBXycbfXp3bvwA4s9wKRMQ5uR/zk2N9IiL2BRanlC7Lbbsnd18HkFJ6Anii6Dx1uR+fSCn9odx6SJIkSYATbUlVpORAnFK6C7ir4PHciHgncCCwFngwpfRmK+pQPBHXibn754DLkCRJkjahfPfp4Zv35ooZc9frPp1vMa7tUcNFR43jAzsNNRRLnVg5Y4g3kFJanlL6Y0rpT60Mw6SUoolbXUGZusLHTZxnfu6477emHpIkSVJecxNtgesUS11FOV2mAYiIXsABwLa5TXOBP6eUVrZhvSRJkqSKyrcUj50ynlmvLG0Iw/mu0xGwcNkqu05LnVhZgTgiPglcBAwE8p/6BCyOiDNSSle3bfUkSZKkyimeaGvgZj3X6zr9swfm2nVa6sRK7jIdEUeTTaC1DDgbODx3Oye37cpcGUmSJKlLyU+09dE97TotdSXltBB/FXga2DultKRg+x8j4kdkSy59FfhdG9ZPkiRJqrh89+naHjV2nZa6kHIm1doeuKooDAOQm1DrKuCdbVUxSZIkqSOpqQnqBvehtkdNw6zTVz44l8vufZbjf/4Itz+5gPr6VOlqSipDOS3EC1rYn4BXNqIukiRJUodWuEZxYdfpgZv15OkFS6jtUUPd4D62FkudRDmB+GrgUxHx45TSssIdEdEf+BRZK7EkSZLUJTXVddo1iqXOqZwu0w8AbwFPRMSXIuKw3O1M4DGyibUeiIj9Cm/tUWlJkiSpUgq7TgOuUSx1YuW0EN9V8PO3ybpIw7rll0YWlYlcmW6trp0kSZLUAeW7Tp9+3aNE4ERbUidVTiD+VLvVQpIkSepE8l2nx04Zz8Jlq/jZA3Ndo1jqhCKl6p4JLyLqgHnz5s2jrq6uwrWRJElSZ1Nfn7j9yQU8vWAJV8yY29BaDFDbo4Zbp4xn2y36VrCGUnWbP38+o0aNAhiVUppfuK+cFmJJkiRJRRqbaAvWdZ+e9cpSALtPSx1Q2YE4IrYC9gQG0sikXCmlX7ZBvSRJkqROo3CirZVr6p15WuokSg7EEVED/BCYRPOzUxuIJUmSVHUKJ9pqaubpsXafljqUcpZd+n/AZOC3wPFks0h/GfgcMBv4B3BwW1dQkiRJ6gzyXadvnTKeXYb3X6/7NGSh+NWlKytUO0mNKafL9PHA7SmlT0bE4Ny2mSmleyPiV8DjwB7AvW1dSUmSJKkzqKmJhhbgwu7TE3cfQbca6N2jO/X1yW7TUgdRTgvxtsDtuZ/zX3f1AEgpLQeuIutOLUmSJFW1fPfpkYN784m9R3Llg3O59J5nOfqKh7j9yQXU11f3Si9SR1FOIF4BrMn9vAxIwJYF+xcA72ijekmSJEmdVr779KXH7NboWOL5ry+vcA0lQXmB+DlgNEBKaQ3wLPCBgv0HAa+0XdUkSZKkzqumJnhr9doNlmI6ad9tmfXKUuYuXGZLsVRh5YwhvheYQDa5FsCvgPMiYmuyCbbGAxe2bfUkSZKkzmur/rUuxSR1YOW0EF8InBIRvXKPvwVcBrwL2Am4ApjattWTJEmSOq/8WOLaHjVNLsVk92mpckpuIU4pvQy8XPB4LTAld5MkSZJUJD+WeOyU8cx6ZWlDGM7PPB0BC5etom5wH1uJpQoop8u0JEmSpDIVL8U0cLOe63Wd/tkDc+06LVVIs12mI2J4RCyIiO+1UO6iiPi/iNiqbasnSZIkdQ357tMf3dOu01JH0dIY4s8CPYFzWyg3DeiVKy9JkiSpSL779Lh3bL7ezNNbD+jJN8bXMnjhI/DabKivb+YsktpSS4H4A8ANKaUlzRXK7f898KG2qpgkSZLU1dTUBHWD+1DbI/tv+NYDevLr9y7kiL8fy4DrJsDl4+Hpmw3F0ibSUiB+JzCzxHM9misvSZIkqQmFM0+fsUd3Rj1wOqxZke1cswKmT4ZFcypbSalKtBSIewKrSzzXarJu05IkSZKakO86feuU8Rw0Iq0Lw3lrVsCyBZWpnFRlWppl+lVgTInn2i5XXpIkSVIzGmaejndAj97rh+IevaHv0MpVTqoiLbUQ/w04OiKaDc4R0QM4BniorSomSZIkdXmDRsOEy7MQDNn9hMuz7ZLaXUstxJcDRwJXRcRJKaUNuk/nwvCVwEhgUttXUZIkSeqiampg7GEwecesm3TfoVkYrmmp3UpSW2g2EKeU7omIK4GTgH0i4pfAY8ASoB+wG/AJoA74WUrp3vatriRJktTF1NTAkDHZTdIm1VILMcDJwMvA/wOmAqlgXwArga+TrUUsSZIkSVKn0GIgTikl4GsR8QOydYZ3BvqTtRL/G/hTSmlhu9ZSkiRJkqQ2VkoLMQC50Ht1+1VFkiRJkqRNx9H6kiRJkqSqZCCWJEmSJFUlA7EkSZIkqSoZiCVJkiRJVclALEmSJEmqSgZiSZIkSVJVKjkQR8SsiDgrIoa2Z4UkSZIkSdoUymkhXgN8C3g+Iv4QEf8TEbYwS5IkSZI6pZIDbUppJ2Af4BfA+4CbgBci4hsRMbqd6idJkiRJUrsoq4U3pfS3lNKngWHAJGAe8BVgVkTcGxHHRUSvdqinJEmSJEltqlVdnlNKb6WUrkop7QuMBa4FDgB+BfxfRHw/IrZpu2pKkiRJktS2Wj0GOCK6RcQE4CLgaCAB9wF/Az4P/CciPtImtZQkSZIkqY2VHYgjYmxEfBd4CbgB2BO4EHhnSumglNKHyFqNnwG+05aVlSRJkiSprXQvtWBEnAScCOyd23Q3cAVwU0rp7cKyKaVnI+JS4GdtVVFJkiRJktpSyYEY+CmwALgA+GlKaX4L5Z8iG1MsSZIkSVKHU04gngjcnFJaW0rhlNIjwCOtqpUkSZIkSe2spDHEEdEX+B7ZZFmSJEmSJHV6JQXilNIyYDCwvH2rI0mSJEnSplHOLNN/I5tRWpIkSZKkTq+cQPxl4KiI+FRERHtVSJIkSZKkTaGcSbUuAt4gW0rpOxExB3irqExKKR3YVpWTJEmS1Ar19bBoDixdAP2GwqDRUFNOW5hUHcoJxNsCCXg+93irtq+OJEmSpI1SXw9P3wzTJ8OaFdCjN0y4HMYeZiiWipQciFNKde1YD0mSJEltYdGcdWEYsvvpk2HyjjBkTGXrJnUwfkUkSZIkdSVLF6wLw3lrVsCyBZWpj9SBldNlGoCIqAMOIusy/euU0vyI6AkMBRaklFa3bRUlSZIklazf0KybdGEo7tEb+g6tXJ2kDqqsFuKI+DYwG7gCOI9sXDFALfAUcEqb1k6SJElSeQaNzsYM9+idPc6PIR40urL1kjqgkluII2Iy8CXgUuAW4M78vpTSkoj4I3AY8P02rqMkSZKkUtXUZBNoTd4x6ybd11mmpaaU02X6FGB6Sum0iBjcyP7Hgc+3TbUkSZIktVpNTTaBlpNoSc0q52uidwJ3NbN/ITBk46ojSZIkSdKmUU4gXgn0aWb/SGDxRtVGkiRJkqRNpJxA/AgwobEdEVELfAL4S1tUSpIkSZKk9lZOIP4u8J6I+BWwa27b0Ig4FLgfGAFc2LbVkyRJkiSpfZQ8qVZK6e6I+CxwCXBcbvOvcvergU+nlB5q4/pJkiRJktQuypllmpTSFbnllT4KjAWCbF3i61JKL7VD/SRJkiRJahdlBWKAlNIC4AftUBdJkiRJkjaZkscQR8TaiDiumf1HR8TatqmWJEmSJEntq5xJtWIj90uSJEmS1GGUE4hbsg2wtA3PJ0mSJElSu2l2DHFEfAT4SMGmkyPioEaKDgIOAh5sw7pJkiRJktRuWppUaxxwQu7nBOyXuxVbBvwV+HxbVUySJEmSpPbUbJfplNK5KaWalFIN2Rjhj+cfF936p5QOSSk9u2mqLUmSJEnSxiln2aVRwKvtVRFJkiRJkjalcibVmgtMaGqnyy5JkiRJkjoTl12SJEmSJFUll12SJEmSJFUll12SJEmSJFWlii67FBHDgFOBdwN7An2B96WU7m/huBrgeGBiro6DgHnAb4DvpZRWlVMPSZIkSVL1qfSyS9sDZwEjgMfLOG4z4OfAEOAnwGnAI8D5wJ/KrIMkSZIkqQqVu+zSwjZ+/pnAkJTS6xFxODC9xONWA+9NKf21YNtPI2I+cG5EHNBSK7MkSZKkMtXXw6I5sHQB9BsKg0ZDTVtOSyRtWiX/9qaUnkspvdWWT55SWppSer0Vx60uCsN5+UC9w8bVTJIkSdJ66uvh6Zvh8vHwi//J7p++OdsudVLltBATEQOBk8jG/A5kw0CdUkoHtlHdWmNo7v61CtZBkiRJ6noWzYHpk2HNiuzxmhXZ48k7wpAxla2b1EolB+KIGAn8BdgaeBPoDyxiXTB+DVjeDnUsx5lkdbuzsZ0RsTmwedHmEe1bJUmSJKkLWLpgXRjOW7MCli0wEKvTKqfD/9fJwuSBwBiySbaOJgvG3yJbg3h8G9evZBHxVbKln85MKb3ZRLHTyGajLrw9sEkqKEmSJHVm/YZCj97rb+vRG/oObby81AmUE4gPBH6aUrqPbAkmgEgpvZVSOht4Avh2W1ewFBFxNFlgvzyldEUzRb9PNjlY4a1iIV6SJEnqNAaNhgmXrwvFPXpnjweNrmy9pI1QzhjiwcC/cz+vyd0XfkV0FzC1LSpVjog4GPglcDPwuebKppQWA4uLjm+vqkmSJEldR00NjD0sGzO8bEHWMuws0+rkygnEC4FBuZ+XAiuBuoL9PVk/ILe7iHg32czSfweOSSmt3ZTPL0mSJFWVmppsvLBjhtVFlPN1zpPAuyCbShp4BDglIraJiDrgZODpNq8hEBGjI2J00bYdgD8B84HDUkorGjtWkiRJkqTGlNNCfBNwRkT0zoXP84A7yCamgmxc8cRyKxAR5+R+zK8d/ImI2BdYnFK6LLftntx9Xe6YfrnnHgh8F/hQUdfnx1NKj5dbF0mSJElS9Sg5EKeUfgT8qODxvRHxHuA4YC0wPaX011bU4fyixyfm7p8DLqNxg4F35H6+oJH95wIGYkmSJElSk8ppId5ASukfwD828hwtzmqVUqorejyfbNknSZIkSZJaxSnhJEmSJElVqcVAHBHviIhTI+KzEbFlwbbfRMSCiFgeEX+OCNfzlSRJkiR1Gs12mY6IscDfgH5kXZSnRsR+wG3AKOBNoB4YD9wVEe9NKc1s3ypLkiRJkrTxWmohPpNsfeHTgKOAxcANwGbA3imlgSmlfsChwBrgy+1WU0mSJEmS2lBLk2rtD/w0pfQDgIhYTrb275kppUfyhVJKd0XET8lmnJYkSZIkqcNrqYV4a9ZfvuiJ3P1TjZT9N9lySJIkSZIkdXgtBeJewIqCx/mfVzZSdlUJ55MkSZIkqUMwwEqSJEmSqlJLY4gB/jsihuZ+3gxIwEcjYlxRuT3asmKSJEmSJLWnUgLxcWw4WdbkJsqmjauOJEmSJEmbRkuB+H2bpBaSJEmSJG1izQbilNKfN1VFJEmSJEnalJxUS5IkSZJUlUoZQyxJkiRJ7ae+HhbNgaULoN9QGDQaamy7U/szEEuSJEmqnPp6ePpmmD4Z1qyAHr1hwuUw9jBDsdqdv2GSJEmSKmfRnHVhGLL76ZOz7VI7MxBLkiRJqpylC9aF4bw1K2DZgsrUR1XFQCxJkiSpcvoNzbpJF+rRG/oOrUx9VFUMxJIkSZIqZ9DobMxwPhTnxxAPGl3ZeqkqOKmWJEmSpMqpqckm0Jq8Y9ZNuq+zTGvTMRBLkiRJqqyaGhgyJrtJm5Bfu0iSJEmSqpKBWJIkSZJUlQzEkiRJkqSqZCCWJEmSJFUlA7EkSZIkqSoZiCVJkiRJVclALEmSJEmqSgZiSZIkSVJVMhBLkiRJkqqSgViSJEmSVJUMxJIkSZKkqmQgliRJkiRVJQOxJEmSJKkqGYglSZIkSVXJQCxJkiRJqkoGYkmSJElSVepe6QpIkiRJUruor4dFc2DpAug3FAaNhhrbBLWOgViSJElS11NfD0/fDNMnw5oV0KM3TLgcxh5mKFYDfxMkSZIkdT2L5qwLw5DdT5+cbZdyDMSSJEmSup6lC9aF4bw1K2DZgsrURx2SgViSJElS19NvaNZNulCP3tB3aGXqow7JQCxJkiSp6xk0OhsznA/F+THEg0ZXtl7qUJxUS5IkSVLXU1OTTaA1ecesm3RfZ5nWhgzEkiRJkrqmmhoYMia7SY3w6xFJkiRJUlUyEEuSJEmSqpKBWJIkSZJUlQzEkiRJkqSqZCCWJEmSJFUlA7EkSZIkqSoZiCVJkiRJVclALEmSJEmqSgZiSZIkSVJVMhBLkiRJkqqSgViSJEmSVJUMxJIkSZKkqmQgliRJkiRVJQOxJEmSJKkqGYglSZIkSVXJQCxJkiRJqkrdK10BSZIkSepU6uth0RxYugD6DYVBo6HGtsbOyEAsSZIkSaWqr4enb4bpk2HNCujRGyZcDmMPMxR3Ql4xSZIkSSrVojnrwjBk99MnZ9vV6RiIJUmSJKlUSxesC8N5a1bAsgWVqY82ioFYkiRJkkrVb2jWTbpQj97Qd2hl6qONYiCWJEmSpFINGp2NGc6H4vwY4kGjK1svtYqTakmSJElSqWpqsgm0Ju+YdZPu6yzTnZmBWJIkSZLKUVMDQ8ZkN3Vqfo0hSZIkSapKBmJJkiRJUlUyEEuSJEmSqpKBWJIkSZJUlQzEkiRJkqSqZCCWJEmSJFUlA7EkSZIkqSoZiCVJkiRJVclALEmSJEmqSgZiSZIkSVJVMhBLkiRJkqqSgViSJEmSVJUMxJIkSZKkqlTRQBwRwyLigoi4LyKWRkSKiAPKOH6HiLg9IpZFxKKI+EVEDGm/GkuSJEmSuopKtxBvD5wFjAAeL+fAiBgBzABGA18FLgQOA+6MiB5tXE9JkiRJUhfTvcLPPxMYklJ6PSIOB6aXcexXgd7AuJTSSwAR8QhwF/AJ4OdtXFdJkiRJUhdS0RbilNLSlNLrrTz8COCP+TCcO9/dwCzgqLaonyRJkiRtMvX18NpsmPdAdl9fX+kadXmVbiFulYgYDmwJ/KOR3Y8AhzRx3ObA5kWbR7Rl3SRJkiSpbPX18PTNMH0yrFkBPXrDhMth7GFQU+mRrl1XZ31nh+XuX25k38vAlhHRrZF9pwHzim4PtEcFJUmSJKlki+asC8OQ3U+fnG1Xu+msgbh37n5VI/tWFpUp9H1gVNFtfFtXTpIkSZLKsnTBujCct2YFLFtQmfpUiU7ZZRrI/6b0amRfbVGZBimlxcDiwm0R0Zb1kiRJkqTy9RuadZMuDMU9ekPfoZWrUxXorC3E+a7SwxrZNwx4NaW0dhPWR5IkSZJab9DobMxwj1xH1/wY4kGjK1uvLq5TthCnlF6KiIXAno3s/i/g0U1bI0mSJEnaCDU12QRak3fMukn3HZqFYSfUaledIhBHxGiAlFLhiPIbgE9ExPCCdYgPBN4JfHfT11KSJEmSNkJNDQwZk920SVQ8EEfEObkfd8jdfyIi9gUWp5Quy227J3dfV3DoN4GPAvdFxA+AvsCXgMeAX7ZrpSVJkiRJnV7FAzFwftHjE3P3zwGX0YSU0gsRsT9wEXABsBq4BTg9pbS6PSoqSZIkSeo6Kh6IU0otTvOcUqprYvuTwKFtXSdJkiRJUtfnCG1JkiRJUlUyEEuSJEmSqpKBWJIkSZJUlQzEkiRJkqSqZCCWJEmSJFUlA7EkSZIkqSoZiCVJkiRJVclALEmSJEmqSgZiSZIkSVJVMhBLkiRJkqqSgViSJEmSVJUMxJIkSZKkqtS90hWQJEmSJLWj+npYNAeWLoB+Q2HQaKixbRQMxJIkSZLUddXXw9M3w/TJsGYF9OgNEy6HsYcZirHLtCRJkiR1XYvmrAvDkN1Pn5xtl4FYkiRJkrqspQvWheG8NStg2YLK1KeDMRBLkiRJUlfVb2jWTbpQj97Qd2hl6tPBGIglSZIkqasaNDobM5wPxfkxxINGV7ZeHYSTakmSJElSV1VTk02gNXnHrJt0X2eZLmQgliRJkqSurKYGhozJblqPXwtIkiRJkqqSgViSJEmSVJUMxJIkSZKkqmQgliRJkiRVJQOxJEmSJKkqGYglSZIkSVXJQCxJkiRJqkoGYkmSJElSVTIQS5IkSZKqkoFYkiRJklSVDMSSJEmSpKpkIJYkSZIkVSUDsSRJkiSpKhmIJUmSJElVyUAsSZIkSapKBmJJkiRJUlXqXukKSJIkSZI6ofp6WDQHli6AfkNh0Gio6VxtrgZiSZIkSVJ56uvh6Zth+mRYswJ69IYJl8PYwzpVKO48NZUkSZIkdQyL5qwLw5DdT5+cbe9EDMSSJEmSpPIsXbAuDOetWQHLFlSmPq1kIJYkSZIklaff0KybdKEevaHv0MrUp5UMxJIkSZKk8gwanY0Zzofi/BjiQaMrW68yOamWJEmSJKk8NTXZBFqTd8y6Sfd1lmlJkiRJUrWoqYEhY7JbJ9W54rskSZIkSW3EQCxJkiRJqkoGYkmSJElSVTIQS5IkSZKqkoFYkiRJklSVDMSSJEmSpKpkIJYkSZIkVSUDsSRJkiSpKhmIJUmSJElVyUAsSZIkSapKBmJJkiRJUlUyEEuSJEmSqpKBWJIkSZJUlQzEkiRJkqSqZCCWJEmSJFUlA7EkSZIkqSp1r3QFOoBuAC+++GKl6yFJkiRJamMFWa9b8b5IKW3a2nQwEbEv8ECl6yFJkiRJalfjU0oPFm4wEEf0AvYCXgbWVrg6jRlBFtjHAzZjd11e5+rgda4OXufq4HWuDl7n6uB17vq6AcOAv6eUVhXuqPou07k35MEWC1ZIROR/fDGlNL+CVVE78jpXB69zdfA6Vwevc3XwOlcHr3PVmNPYRifVkiRJkiRVJQOxJEmSJKkqGYglSZIkSVXJQNzxLQbOzd2r61qM17kaLMbrXA0W43WuBovxOleDxXidq8FivM5Vq+pnmZYkSZIkVSdbiCVJkiRJVclALEmSJEmqSgZiSZIkSVJVMhBLkiRJkqqSgbiDioheEfHtiPi/iFgREX+LiAMrXS+1LCL2iogfRsRTEbE8Ip6PiGsjYruicvdHRGrkdm0j5/T3oYOJiAOauH4pIsYWld0nIh6MiLciYkFEXBIRmzVyTq9zBxMRVzdznVNEDM+V8/PcSUTEsIi4ICLui4iluet0QBNlPxwR/4yIlbm/5VMjonsj5TaPiCsiYmHu7/69ETFuY86pjVPKdY6IwRHxpYh4IHftFkfEQxHx0UbOd0IzfwdqGynvdW5npX6WI2J+E9ftgkbK+lmuQl60jutq4Ajg+8CzwAnAbRGxf0rpocpVSyU4C3gv8HvgcWAo8HngXxHxXyml/xSUfR44u+j4+Y2c82r8feiovg/MLNr2f/kfcv+Q3gM8CZwOjAD+H7AtcFjRcVfjde5oLgfuLtoWwE+A+Smllwq2+3nuHLYn+zv9LNnf6H0aKxQRHwT+ANwLfAHYBfgaMCT3OF+uBvhTbv+FwOvAKcD9EbFHSmlOuedUmyjlOr8H+AZwK/B14G2yz+Z1EfG1lNL5jRxzNtlnvdDqwgde502mpM9yzkyyv7mF/l34wM9yFUspeetgN+C/gAScVrCtluwDP6PS9fPW4vXbB+hZtG0MsBK4umDb/cCj/j50zhtwQO66HN5CuVuBF4G+Bdsm5Y59v9e5892AfXPX6qsF2/w8d5Ib0A8YnPv58Nz1OKCRck+S/Se6W8G2rwNrgTEF244q/lsAbAG8AfyyNef0tmmuMzAKGFm0Lci+xHwL6F2w/YTcOcaV8Nxe5w5yjXP75gN/KOF8fpar9GaX6Y7pSGAN8LP8hpTSSuBKYN+IGFapiqllKaW/ppRWF22bTfbHc4fi8hHRPSL6NnNKfx86uIjo10Q3yv7AwWT/kC4r2PVLYBnZP755XufO4ziy/zT9pniHn+eOL6W0NKX0enNlImJHYEfg8pTS2oJdPyIbbnZEwbYjyXqF3FTwHAuB64DDI6JHK86pjVTKdU4pzUspPVe0LZG1/PUG6ho7LiL651oTG9vndd5ESrnGhXLDVTYYrlTAz3KVMhB3TLsBTxf9BxrgEbJvLsdt8hppo0REAFsBrxXt2gFYDizNjSf8aiP/yPr70LH9ClgCrIiIOyNil4J9u5ANTflH4QG5L0weJbu2eV7nTiD3H6KjgL+mlOYX7fbz3HXkP5vFn93/I+vxUfzZnZkLUoUeIWvB2q6gXKnnVGUNzd0X/5sN8ADwJrA8Iq6PiG2K9nudO6ZDyP4+L4+IORFxciNl/CxXKccQd0zDgJca2f5y7n7rTVgXtY2PAcNZf3zhHLKxJ08A/YFjycYybQN8pqCcvw8d02rgeuA2sv807Uo2NvjBiNgrpTSL7NrBumtV6GWy8Wt5XufO4VBgMPDrou1+nruWlj67WxeVvbeJcuTK/qfMc6pCImIQ2bCW+3Otg3nLgavIhkcsBd4NnAa8OyJ2Synlw7PXueN5nOyLjFlkXaA/DVweEYNSSoUTa/lZrlIG4o6pN7Cqke0rC/ark4hsxuEfAg+StSYCkFI6qajoLyLiOuDkiLg4pfRMbru/Dx1QSumvwF8LNv0xIm4m+8Z4KtmXIPlr09T1K7x2XufO4TiyLs/XFW7089zltPTZ3ayobCnXtJxzqgJyPTp+DQwAphTuSyn9nmyyzLzpETGDbBKmL7LuC2+vcweTUvpw4eOIuIrs/2T/GxE/Tim9mdvlZ7lK2WW6Y1oB9Gpke23BfnUCETGU7B/LN4CPppTqWzjke2TdJt9XsM3fh04ipfQY2YzE+SV08temqetXeO28zh1cbmzwR4A7Shy35ue582qPz24551Rl/ICsF8inUkpPtFQ4pXQr8DTr/uaD17nDy437/T5ZcC3sqeVnuUoZiDuml1nXHaNQftv/NbJPHUxEDCDrTjsAODSltKCEw17I3Q8q2ObvQ+fyAuuuX747VVPXr/DaeZ07vsPJ/gNV3F26KX6eO6/2+OyWc05tYhExlWyJnTNTSr8t49DCv/ngde4sNubvs9e4izEQd0yPAmMbman03bn7xzZtdVSuiKgFbgbeCfxPQXfJlmybuy8ct/Qo/j50Jtuy7vr9m2xdyz0LC0RET7LJkx4t2PwoXueO7mNks4P/scTyfp47r0dz98Wf3a3J1hJ/tKjsHrnJEwu9m+z35dlWnFObUER8DpgGXJxSurDMwwv/5oPXubNo6u+zn+UqZCDumK4HepBN6gBkU8UDnwL+kpvFTh1URHQDfkfWDeejKaW/NVKmf+6aFh/3VaCerNttnr8PHVBEbNHItn3JusfeAZAbl3Q38ImiAPQJoC/rj0fzOndguet9EDA9pfRW0T4/z11MSulJsq6wJ+euZd5nya7pDQXbriebROcj+Q0RMQT4KHBTSmlNK86pTSQijgYuJev5cUYz5Rr7m38cMJrc33zwOnc0ETGoeLb/XKPFl8gmR3uoYJef5SrlpFodUErp4Yj4PfCd3JqUc4DjgZFkC8OrY/se8GGyFuJBEfHxgn3LUkp/AHYHfhMRvyX7xrEv2VIuewLfTinNyx/g70OH9buIeItsYq3XgJ2Bk3M/Tysod3auzP0R8TOyb4/PAG5LKTUEJa9zh3c02b+ZjXWX9vPcyUTEObkf82vDfyL3hdbilNJluW1fIusNcEdE/I7sM/55srVHZxWc7nrgb8AvI+JCsr8Bp5A1OkwreupSz6k20NJ1joj/IlsX/nXgHuBjRY2Dd6WUXsn9/NeImAn8k2ypvf8i++zOAi4pemqv8yZSwmf5w8DZEXE9MJ9slYDjyXrwfbZoCTw/y9UqpeStA97IBuV/l2ycwkqyNdAOqnS9vJV07e4HUhO3+bkyo8haB+eTTb6wHHgYON7fh85xI5uB9GGy/0itIVtK5+fANo2U3Rf4S+5av0LWGtHH69x5bmStCK8A3RrZ5+e5k91a+htdUO5w4F+56/QCcC7QvZHzDQR+RvYf6OXAfcDuTTx3Sef01v7XmexLqKbKJOCAgnN9nawr7GKyZffmkQXhQV7nDn2N9yBroHiRbFboJWT/T/ufJs7nZ7kKb5G7oJIkSZIkVRXHEEuSJEmSqpKBWJIkSZJUlQzEkiRJkqSqZCCWJEmSJFUlA7EkSZIkqSoZiCVJkiRJVclALEmSJEmqSgZiSZLUKUTE/Ii4v9L1kCR1HQZiSVLVi4gDIiIV3VZGxNyIuCoidtjI80+LiMPL3dcRRcSBEXFdRLwQEasiYmlEzIyIb0TEiErXT5KkckRKqdJ1kCSpoiLiAOA+4LfArbnNvYFdgUnAGmCXlNJzrTx/An6RUjqhnH0dSUTUAJeTvR/PAb8DZgM9gT2AI4DVKaUt27EO84H5KaUD2us5JEnVpXulKyBJUgfyz5TSNYUbImI2cAkwEbi4IrVqpYjol1Ja2kanm0YWhn8LnJBSWl30XGcAU9vouSRJ2iTsMi1JUvP+L3dfHABPiYg7I+KliFgdES9HxDURUVdQpi7XAgxwfFGX7Cb3FT3PQbnnWZzrxv14RHymuJL58bURsVtE3BERbwKPR8SE3Hk/3diLi4gnI+LZiIim3oCI2BL4ElnL8InFYRggpbQ4pfTFXPkv5p7z4EbO1SsiXo+Ie4u27xYRv4+IV3JdsV+IiN9GxOim6lVw7J4RMT0iXssd+0xEnB0RfvEvSWqW/1BIkrTOZhExJPdzb2Bn4BvAa8ANRWX/H/A34FJgUa7sJOD9EbFLSul1YCHwCeBXwAPAFQXHN7cPgIg4GfhJ7nm+ASwHDgZ+HBGjU0pfKjpkG+Be4Pe5+vYFbgYWACcCPy06/97AjsDZqfkxVB8CaoFfppRWNlMu75fAt3LPeVfRvgnAIOBnBfX4n1x9l+e2PwsMBQ4le1/nNPVEEfEh4MbcMd8juxbvAc4DxgEfLaG+kqQq5RhiSVLVKxhD3JingCNSSk8XHdMnpbS8aNuBwN3AWSml7xRsL3sMcUQMA+YBN6aUjivadwnweWBMSmlubtt8YCTw6ZTSz4rKfxP4CrBTSumpgu0/BT4FbJNS+j+aEBHfA07PvQ83NlWu6JjfkHUz3zqltKhg+11kY463TimtjIjNyFqeE7BbSumlovPUpJTqC15jwxjiiKgF5gOzgPenlN4uOO6LwEXA+1JK95dSZ0lS9bHLtCRJ61xB1gJ7MHAYcBYwBLg1IkYWFsyH4YioiYgBuZblx4A3gXe3QV2OBHoBV0bEkMIbWatvDXBQ0TGLgKsaOddPyQLnSfkNEdEHOBq4rbkwnNM/d7+kjPpfkav/xwqesw44EPh1QUvzoWTv8feKwzBAPgw34WBgK7LXvHnRe5SfHO2QMuosSaoydpmWJGmd2Smluwse3xIRfybrsvxt4Jj8joh4P/A1svBbW3SegW1Ql/xST3c3U2arosdzUkpriwullOZFxN3AJyLiyymlNcBRQD8Kui43Ix+E+5VQNv+c90fELLIQ/oPc5k8BUfScY3L3/yr13AXy79HPmylT/B5JktTAQCxJUjNSSg/nJqh6f35bROwF3Ek2bvXLZF2bV5C1wl5L2/TAyk9y9Ung5SbKzC16/FYz57uCbGzxh8nG655ENrb4TyXU5d+5+92A6SWUz/sp8N2I2IMs8J4A/COl9FgZ52hO/j36EvBoE2Vaav2WJFUxA7EkSS3rTtb9N+84oBvwwZTSvPzGXDfktmgdhmyNX4DXilqtW+sm4FXgpIj4N/Be4NuF426b8SdgJVkL8zdSSqtKfM6rySYDOyn3/NuQTbZVaFbufhzZlwzlyL9Hy9voPZIkVRnHEEuS1Izc0kF9gJkFm/PdkouXKvoqjf/buoxsZuXGNLXvOmAVcG5E9G6kXgMioteGhzUu1036arIxu/n1gq8s8dhXge8CdcDPIqJnI/XpHxEXFx33GvAHsi8QPk/Wgv2bokPvJJvF+4zcRGLF521yOSjgDrKQ/+WI2OA9jIjeEVFyN29JUvVxlmlJUtUrmGX6t6ybjKkXsBNwMtATOCQ/W3FEvBeYQdZCeQXZGsUHA7uSLXX0ZH4m5Fz5u8haZM8FngdSSunaEvZ9imy87QtkyzM9B2wB7AIcDuyYUpqfKzufghmYm3id25G1yAbw5+bKNnJsDXA52dJS88m6hj+be2/GkS1vtDqlNLTouPzM29D0TNsfBq4HlrJu2aUtyML7RSmlm5p6jRFxKFnoXkY2lvhZYHNgLNks1xOcZVqS1BQDsSSp6jWx7FI98DrwIPCtlNLfi445HPhfsuC1giz0nUkWlItD2xjgh8De5CamSilFS/ty+99Ltubxe8mC3mvAM8AtwA/zszWXEohz5e4hGw/9yZTSr5or28TxBwGTydb63ZKsFXsW2RcJP0opvVxUPnL7twP2Syk90MR5/4ushX1fsvfhFbL1mf+3aGmpDV5jROxMNpb7fWRB+g2ytYtvI3uPFiFJUiMMxJIkVZGIuJUszG6dUlqxiZ7zSaBbSmnspng+SZJK5RhiSZKqRK7L9KHANZswDL8f2JFsxmlJkjoUW4glSeriIuLdZGv2Tsnd75Afe9yOz/l+YDTwFbJx1dullJY0f5QkSZuWLcSSJHV9nyWbcKo/8LH2DsM5XwN+TDbZ1RGGYUlSR2QLsSRJkiSpKtlCLEmSJEmqSgZiSZIkSVJVMhBLkiRJkqqSgViSJEmSVJUMxJIkSZKkqvT/ASpItGoZ/RSlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "fig.suptitle(\"Last Sample of Cell 1\")\n",
    "sns.scatterplot(data=hist, x=\"History_Cycle\", y=\"History\")\n",
    "sns.scatterplot(data=target, x=\"Target_Cycle\", y=\"Target\")\n",
    "ax.legend([\"input sequence\", \"output sequence\"])\n",
    "ax.set(xlabel='Battery Cycle', ylabel='Battery Capacity (Ah)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "61f0a539",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Rename\n",
    "df = df.rename(\n",
    "    columns={\n",
    "        \"Cell\": \"cell_id\",\n",
    "        \"Sample\": \"sample_number\",\n",
    "        \"History_Cycle\": \"mesurement_cycle_number\",\n",
    "        \"History\": \"capacity_measured\",\n",
    "        \"Target_Cycle\": \"target_cycle_number\",\n",
    "        \"Target\": \"capacity_target\",\n",
    "        \"Target_Cycle_Expanded\": \"target_cycle_number_expended\",\n",
    "        \"Target_Expanded\": \"target_capacity_expanded\"\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bbace79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('./battery_data_renamed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "deeb6024",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_id</th>\n",
       "      <th>sample_number</th>\n",
       "      <th>mesurement_cycle_number</th>\n",
       "      <th>capacity_measured</th>\n",
       "      <th>target_cycle_number_expended</th>\n",
       "      <th>Target_expanded</th>\n",
       "      <th>target_cycle_number</th>\n",
       "      <th>capacity_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...</td>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[105, 110, 115, 120, 125, 130, 135, 140, 145, ...</td>\n",
       "      <td>[1.7973466099593307, 1.7957194104838883, 1.794...</td>\n",
       "      <td>[105, 150, 195, 240, 285, 330, 375, 420, 465, ...</td>\n",
       "      <td>[1.7973466099593307, 1.7832393947563925, 1.770...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...</td>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[110, 115, 120, 125, 130, 135, 140, 145, 150, ...</td>\n",
       "      <td>[1.7957194104838883, 1.794106179214274, 1.7925...</td>\n",
       "      <td>[110, 155, 200, 245, 290, 335, 380, 425, 470, ...</td>\n",
       "      <td>[1.7957194104838883, 1.7817527911332156, 1.769...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...</td>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[115, 120, 125, 130, 135, 140, 145, 150, 155, ...</td>\n",
       "      <td>[1.794106179214274, 1.7925073295393719, 1.7909...</td>\n",
       "      <td>[115, 160, 205, 250, 295, 340, 385, 430, 475, ...</td>\n",
       "      <td>[1.794106179214274, 1.7802836132202329, 1.7676...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...</td>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[120, 125, 130, 135, 140, 145, 150, 155, 160, ...</td>\n",
       "      <td>[1.7925073295393719, 1.7909232748480652, 1.789...</td>\n",
       "      <td>[120, 165, 210, 255, 300, 345, 390, 435, 480, ...</td>\n",
       "      <td>[1.7925073295393719, 1.7788295719024707, 1.766...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...</td>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[125, 130, 135, 140, 145, 150, 155, 160, 165, ...</td>\n",
       "      <td>[1.7909232748480652, 1.7893544285292378, 1.787...</td>\n",
       "      <td>[125, 170, 215, 260, 305, 350, 395, 440, 485, ...</td>\n",
       "      <td>[1.7909232748480652, 1.7773897124807985, 1.765...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cell_id  sample_number                            mesurement_cycle_number  \\\n",
       "0        1              1  [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...   \n",
       "1        1              2  [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...   \n",
       "2        1              3  [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...   \n",
       "3        1              4  [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...   \n",
       "4        1              5  [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,...   \n",
       "\n",
       "                                   capacity_measured  \\\n",
       "0  [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "1  [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "2  [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "3  [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "4  [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "\n",
       "                        target_cycle_number_expended  \\\n",
       "0  [105, 110, 115, 120, 125, 130, 135, 140, 145, ...   \n",
       "1  [110, 115, 120, 125, 130, 135, 140, 145, 150, ...   \n",
       "2  [115, 120, 125, 130, 135, 140, 145, 150, 155, ...   \n",
       "3  [120, 125, 130, 135, 140, 145, 150, 155, 160, ...   \n",
       "4  [125, 130, 135, 140, 145, 150, 155, 160, 165, ...   \n",
       "\n",
       "                                     Target_expanded  \\\n",
       "0  [1.7973466099593307, 1.7957194104838883, 1.794...   \n",
       "1  [1.7957194104838883, 1.794106179214274, 1.7925...   \n",
       "2  [1.794106179214274, 1.7925073295393719, 1.7909...   \n",
       "3  [1.7925073295393719, 1.7909232748480652, 1.789...   \n",
       "4  [1.7909232748480652, 1.7893544285292378, 1.787...   \n",
       "\n",
       "                                 target_cycle_number  \\\n",
       "0  [105, 150, 195, 240, 285, 330, 375, 420, 465, ...   \n",
       "1  [110, 155, 200, 245, 290, 335, 380, 425, 470, ...   \n",
       "2  [115, 160, 205, 250, 295, 340, 385, 430, 475, ...   \n",
       "3  [120, 165, 210, 255, 300, 345, 390, 435, 480, ...   \n",
       "4  [125, 170, 215, 260, 305, 350, 395, 440, 485, ...   \n",
       "\n",
       "                                     capacity_target  \n",
       "0  [1.7973466099593307, 1.7832393947563925, 1.770...  \n",
       "1  [1.7957194104838883, 1.7817527911332156, 1.769...  \n",
       "2  [1.794106179214274, 1.7802836132202329, 1.7676...  \n",
       "3  [1.7925073295393719, 1.7788295719024707, 1.766...  \n",
       "4  [1.7909232748480652, 1.7773897124807985, 1.765...  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b053a12f",
   "metadata": {},
   "source": [
    "# Input and Output length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6eea01ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "288"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_input_length = df.capacity_measured.map(lambda x: len(x)).max()\n",
    "max_input_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad09c65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_output_length = df.capacity_target.map(lambda x: len(x)).max()\n",
    "max_output_length"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9f843a2",
   "metadata": {},
   "source": [
    "# Padding\n",
    "\n",
    "Since the input and output sequences change size, we need to pad them.\n",
    "We use the length of the longest input and output, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2a06d70f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pad_sequences(\n",
    "    df.capacity_measured,\n",
    "    maxlen=max_input_length,\n",
    "    dtype='float64',\n",
    "    padding='post',\n",
    "    value=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a532f6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pad_sequences(\n",
    "    df.capacity_target,\n",
    "    maxlen=max_output_length,\n",
    "    dtype='float64',\n",
    "    padding='post',\n",
    "    value=0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c69ae75",
   "metadata": {},
   "source": [
    "# Reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "221d2c0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10686, 288)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d61dbad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_features = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3bab2cc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.reshape(-1, max_input_length, number_of_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "52e7dd06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10686, 39)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "167d423e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10686, 39, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.reshape(-1, max_output_length, number_of_features).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c1eb27",
   "metadata": {},
   "source": [
    "# Split Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "19e94262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 20, 25, 30, 35])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## ORIGINAL TEST ID's\n",
    "\n",
    "test_cells = [15,20,25,30,35]\n",
    "\n",
    "df_test = df[pd.DataFrame(df.cell_id.tolist()).isin(test_cells).any(True).values]\n",
    "df_test.cell_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8b270e6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13, 42])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Validation Set\n",
    "\n",
    "val_cells = [42, 13]\n",
    "\n",
    "df_val = df[pd.DataFrame(df.cell_id.tolist()).isin(val_cells).any(True).values]\n",
    "df_val.cell_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "16e63419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 14, 16, 17, 18, 19,\n",
       "       21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39, 40,\n",
       "       41, 43, 44, 45, 46, 47])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df[~pd.DataFrame(df.cell_id.tolist()).isin(test_cells + val_cells).any(True).values]\n",
    "df_train.cell_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3c957147",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train[[\"capacity_measured\", \"capacity_target\"]]\n",
    "df_test = df_test[[\"capacity_measured\", \"capacity_target\"]]\n",
    "df_val = df_val[[\"capacity_measured\", \"capacity_target\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "67ebdbff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>capacity_measured</th>\n",
       "      <th>capacity_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[1.7973466099593307, 1.7832393947563925, 1.770...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[1.7957194104838883, 1.7817527911332156, 1.769...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[1.794106179214274, 1.7802836132202329, 1.7676...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[1.7925073295393719, 1.7788295719024707, 1.766...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[1.834012342776987, 1.8321873038112761, 1.8303...</td>\n",
       "      <td>[1.7909232748480652, 1.7773897124807985, 1.765...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10681</th>\n",
       "      <td>[1.84724471159825, 1.845306403634897, 1.843378...</td>\n",
       "      <td>[1.4027216711179482, 1.3580654511370822, 1.309...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10682</th>\n",
       "      <td>[1.84724471159825, 1.845306403634897, 1.843378...</td>\n",
       "      <td>[1.3979441697822066, 1.3528860367290538, 1.303...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10683</th>\n",
       "      <td>[1.84724471159825, 1.845306403634897, 1.843378...</td>\n",
       "      <td>[1.3931186077270967, 1.3476662199521636, 1.298...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10684</th>\n",
       "      <td>[1.84724471159825, 1.845306403634897, 1.843378...</td>\n",
       "      <td>[1.3882458358804528, 1.342406851734246, 1.2921...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10685</th>\n",
       "      <td>[1.84724471159825, 1.845306403634897, 1.843378...</td>\n",
       "      <td>[1.383326705170109, 1.3371087830031347, 1.2861...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9180 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       capacity_measured  \\\n",
       "0      [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "1      [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "2      [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "3      [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "4      [1.834012342776987, 1.8321873038112761, 1.8303...   \n",
       "...                                                  ...   \n",
       "10681  [1.84724471159825, 1.845306403634897, 1.843378...   \n",
       "10682  [1.84724471159825, 1.845306403634897, 1.843378...   \n",
       "10683  [1.84724471159825, 1.845306403634897, 1.843378...   \n",
       "10684  [1.84724471159825, 1.845306403634897, 1.843378...   \n",
       "10685  [1.84724471159825, 1.845306403634897, 1.843378...   \n",
       "\n",
       "                                         capacity_target  \n",
       "0      [1.7973466099593307, 1.7832393947563925, 1.770...  \n",
       "1      [1.7957194104838883, 1.7817527911332156, 1.769...  \n",
       "2      [1.794106179214274, 1.7802836132202329, 1.7676...  \n",
       "3      [1.7925073295393719, 1.7788295719024707, 1.766...  \n",
       "4      [1.7909232748480652, 1.7773897124807985, 1.765...  \n",
       "...                                                  ...  \n",
       "10681  [1.4027216711179482, 1.3580654511370822, 1.309...  \n",
       "10682  [1.3979441697822066, 1.3528860367290538, 1.303...  \n",
       "10683  [1.3931186077270967, 1.3476662199521636, 1.298...  \n",
       "10684  [1.3882458358804528, 1.342406851734246, 1.2921...  \n",
       "10685  [1.383326705170109, 1.3371087830031347, 1.2861...  \n",
       "\n",
       "[9180 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ee7665",
   "metadata": {},
   "source": [
    "# Preprocess Functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "df8fc65c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_df(df, scaler=None):\n",
    "    x = df.capacity_measured\n",
    "    #normalize data with max/min values \n",
    "    # x = df.values\n",
    "    # x = x.astype(np.float64)\n",
    "    if scaler is None:\n",
    "        scaler = preprocessing.StandardScaler().fit(x)\n",
    "    x = scaler.transform(x)\n",
    "    print(x.shape)\n",
    "    \n",
    "    return x, scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "5bedb425",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_data(df: DataFrame, split_ids: dict={}) -> dict:\n",
    "    \"\"\"\n",
    "    Takes a dataframe\n",
    "    Left pads the input and target data\n",
    "    Reshapes the input and target data to a 3D vector\n",
    "    \n",
    "    Returns a tuple of the preprocessed data as: \n",
    "    ```\n",
    "    {\n",
    "      \"train\": {\"x_train\": x_1, \"y_train\": y_1},\n",
    "      \"test\": {\"x_test\": x_3, \"y_test\": y_2},\n",
    "      \"train\": {\"x_train\": x_3, \"y_train\": y_3},\n",
    "    }\n",
    "    ```\n",
    "    \"\"\"\n",
    "    max_input_length = df.capacity_measured.map(lambda x: len(x)).max()\n",
    "    max_output_length = df.capacity_target.map(lambda x: len(x)).max()\n",
    "    \n",
    "    # Test Set\n",
    "    test_cells = [15,20,25,30,35]\n",
    "\n",
    "    df_test = df[pd.DataFrame(df.cell_id.tolist()).isin(test_cells).any(True).values]\n",
    "    \n",
    "    ## Validation Set\n",
    "\n",
    "    val_cells = [42, 13]\n",
    "\n",
    "    df_val = df[pd.DataFrame(df.cell_id.tolist()).isin(val_cells).any(True).values]\n",
    "    \n",
    "    # Training Set\n",
    "    df_train = df[~pd.DataFrame(df.cell_id.tolist()).isin(test_cells + val_cells).any(True).values]\n",
    "    \n",
    "    # Drop all cloumns but x and y\n",
    "    df_train = df_train[[\"capacity_measured\", \"capacity_target\", \"mesurement_cycle_number\", \"target_cycle_number\"]]\n",
    "    df_test = df_test[[\"capacity_measured\", \"capacity_target\", \"mesurement_cycle_number\", \"target_cycle_number\"]]\n",
    "    df_val = df_val[[\"capacity_measured\", \"capacity_target\", \"mesurement_cycle_number\", \"target_cycle_number\"]]\n",
    "            \n",
    "    # Normalize\n",
    "    #df_train, scaler = normalize_df(df_train)\n",
    "    #df_test, _ = normalize_df(df_test, scaler)\n",
    "    #df_val, _ = normalize_df(df_val, scaler)\n",
    "    \n",
    "    \n",
    "    # Padding and Reshaping\n",
    "    x_train = pad_sequences(\n",
    "        df_train.capacity_measured,\n",
    "        maxlen=max_input_length,\n",
    "        dtype='float64',\n",
    "        padding='post'\n",
    "    ).reshape(-1, max_input_length, number_of_features)\n",
    "    \n",
    "    y_train = pad_sequences(\n",
    "        df_train.capacity_target,\n",
    "        maxlen=max_output_length,\n",
    "        dtype='float64',\n",
    "        padding='post',\n",
    "        value=0\n",
    "    ).reshape(-1, max_output_length, number_of_features)\n",
    "    \n",
    "    x_val = pad_sequences(\n",
    "        df_val.capacity_measured,\n",
    "        maxlen=max_input_length,\n",
    "        dtype='float64',\n",
    "        padding='post'\n",
    "    ).reshape(-1, max_input_length, number_of_features)\n",
    "    \n",
    "    y_val = pad_sequences(\n",
    "        df_val.capacity_target,\n",
    "        maxlen=max_output_length,\n",
    "        dtype='float64',\n",
    "        padding='post',\n",
    "        value=0\n",
    "    ).reshape(-1, max_output_length, number_of_features)\n",
    "    \n",
    "    x_test = pad_sequences(\n",
    "        df_test.capacity_measured,\n",
    "        maxlen=max_input_length,\n",
    "        dtype='float64',\n",
    "        padding='post'\n",
    "    ).reshape(-1, max_input_length, number_of_features)\n",
    "    \n",
    "    y_test = pad_sequences(\n",
    "        df_test.capacity_target,\n",
    "        maxlen=max_output_length,\n",
    "        dtype='float64',\n",
    "        padding='post',\n",
    "        value=0\n",
    "    ).reshape(-1, max_output_length, number_of_features)\n",
    "    \n",
    "    return {\n",
    "        \"train\": {\n",
    "            \"x_train\": x_train,\n",
    "            \"y_train\": y_train,\n",
    "            \"mesurement_cycle_number\": df_train[\"mesurement_cycle_number\"],\n",
    "            \"target_cycle_number\": df_train[\"target_cycle_number\"]\n",
    "        },\n",
    "        \"val\": {\n",
    "            \"x_val\": x_val,\n",
    "            \"y_val\": y_val,\n",
    "            \"mesurement_cycle_number\": df_val[\"mesurement_cycle_number\"],\n",
    "            \"target_cycle_number\": df_val[\"target_cycle_number\"]\n",
    "        },\n",
    "        \"test\": {\n",
    "            \"x_test\": x_test,\n",
    "            \"y_test\": y_test,\n",
    "            \"mesurement_cycle_number\": df_test[\"mesurement_cycle_number\"],\n",
    "            \"target_cycle_number\": df_test[\"target_cycle_number\"]\n",
    "        }\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "32f20f93",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = preprocess_data(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "27dd3b58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9180, 288, 1)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"train\"][\"x_train\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2255433d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(423, 39, 1)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"val\"][\"y_val\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "877ff058",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.83401234],\n",
       "       [1.8321873 ],\n",
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       "       [0.        ]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"train\"][\"x_train\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db0aed1",
   "metadata": {},
   "source": [
    "# The Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f0e656",
   "metadata": {},
   "source": [
    "The requirements to use the cuDNN implementation are:\n",
    "\n",
    "  - activation == tanh\n",
    "  - recurrent_activation == sigmoid\n",
    "  - recurrent_dropout == 0\n",
    "  - unroll is False\n",
    "  - use_bias is True\n",
    "  - **Inputs, if use masking, are strictly right-padded.**\n",
    "  - Eager execution is enabled in the outermost context.\n",
    "  \n",
    "https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "3aacb87d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#define custom loss function\n",
    "class CustomLoss:\n",
    "    @staticmethod\n",
    "    def RMSE(y_true, y_pred):\n",
    "        return kb.sqrt(tf.keras.losses.mean_squared_error(y_true, y_pred))\n",
    "    @staticmethod\n",
    "    def MaskedRMSE(y_true,y_pred):\n",
    "        isMask = kb.equal(y_true, 0)\n",
    "        isMask = kb.all(isMask, axis=-1)\n",
    "        isMask = kb.cast(isMask, dtype=kb.floatx())\n",
    "        isMask = 1 - isMask\n",
    "        isMask = kb.reshape(isMask,tf.shape(y_true))\n",
    "        masked_squared_error = kb.square(isMask * (y_true - y_pred))\n",
    "        masked_mse = kb.sum(masked_squared_error, axis=-1) / (kb.sum(isMask, axis=-1)+kb.epsilon())\n",
    "        return kb.sqrt(masked_mse)\n",
    "    @staticmethod\n",
    "    def MaskedMSE(y_true,y_pred):\n",
    "        isMask = kb.equal(y_true, 0)\n",
    "        isMask = kb.all(isMask, axis=-1)\n",
    "        isMask = kb.cast(isMask, dtype=kb.floatx())\n",
    "        isMask = 1 - isMask\n",
    "        isMask = kb.reshape(isMask,tf.shape(y_true))\n",
    "        masked_squared_error = kb.square(isMask * (y_true - y_pred))\n",
    "        masked_mse = kb.sum(masked_squared_error, axis=-1) / (kb.sum(isMask, axis=-1)+kb.epsilon())\n",
    "        return masked_mse\n",
    "    @staticmethod\n",
    "    def MaskedMAE(y_true,y_pred):\n",
    "        isMask = kb.equal(y_true, 0)\n",
    "        isMask = kb.all(isMask, axis=-1)\n",
    "        isMask = kb.cast(isMask, dtype=kb.floatx())\n",
    "        isMask = 1 - isMask\n",
    "        isMask = kb.reshape(isMask,tf.shape(y_true))\n",
    "        masked_AE = kb.abs(isMask * (y_true - y_pred))\n",
    "        masked_mae = kb.sum(masked_AE, axis=-1)/ (kb.sum(isMask, axis=-1)+kb.epsilon())\n",
    "        return masked_mae\n",
    "    #numpy function wrapper\n",
    "    @staticmethod\n",
    "    @tf.function\n",
    "    def MaskedMAPE(y_true,y_pred):\n",
    "        return tf.py_function(CustomLoss.numpyMaskedMAPE ,(y_true, y_pred), tf.double)\n",
    "    @staticmethod\n",
    "    def numpyMaskedMAPE(y_true,y_pred):\n",
    "        MapeLst=list()\n",
    "        for elm_t,elm_p in zip(y_true,y_pred):\n",
    "            y_t=elm_t[0:np.count_nonzero(elm_t),:]\n",
    "            y_p=elm_p[0:np.count_nonzero(elm_t),:]\n",
    "            MapeLst.append(np.mean(((np.abs(y_t - y_p)+1e-10) / y_t)) * 100)\n",
    "        return np.array(MapeLst,dtype=float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "36e4e9b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model(x_train, max_input_length, output_length):\n",
    "    input_shape = x_train.shape[1:]\n",
    "    lstm_width = max_input_length\n",
    "    bottleneck_width = 64\n",
    "    dp_lvl = 0.2\n",
    "    learning_rate = 0.001\n",
    "    number_of_features = 1\n",
    "    hiden_nodes = 64\n",
    "    \n",
    "    timesteps = x_train.shape[1]\n",
    "    features = x_train.shape[2]\n",
    "    \n",
    "    model = keras.Sequential([\n",
    "        Input(shape=(input_shape)),\n",
    "        Masking(mask_value=0., input_shape=(timesteps, features), name=\"masking\"),\n",
    "         # Encoder\n",
    "        LSTM(\n",
    "            hiden_nodes, \n",
    "            return_sequences=True,\n",
    "            name=\"encoder-1\"\n",
    "        ),\n",
    "        LSTM(\n",
    "            hiden_nodes, \n",
    "            return_sequences=False,\n",
    "            name=\"encoder-2\"\n",
    "        ),\n",
    "        RepeatVector(\n",
    "            output_length,\n",
    "            name=\"repeat-vector\"\n",
    "        ),\n",
    "        LSTM(\n",
    "            hiden_nodes, \n",
    "            return_sequences=True,\n",
    "            name=\"decoder-1\"\n",
    "        ),\n",
    "        LSTM(\n",
    "            hiden_nodes, \n",
    "            return_sequences=True,\n",
    "            name=\"decoder-2\"\n",
    "        ),\n",
    "        tf.keras.layers.Dense(\n",
    "            hiden_nodes*2, \n",
    "            activation=\"relu\",\n",
    "            name=\"dense-1\"\n",
    "        ),\n",
    "        tf.keras.layers.Dense(\n",
    "            hiden_nodes/4, \n",
    "            activation=\"relu\",\n",
    "            name=\"dense-2\"\n",
    "        ),\n",
    "        tf.keras.layers.Dense(\n",
    "            number_of_features,\n",
    "            activation=\"linear\",\n",
    "            name=\"output\"\n",
    "        )\n",
    "    ])\n",
    "     \n",
    "    optimizer = Adam(learning_rate=learning_rate)\n",
    "    model.compile(\n",
    "        optimizer=optimizer,\n",
    "        loss=CustomLoss.MaskedMAE,\n",
    "        metrics=[CustomLoss.MaskedMAPE,CustomLoss.MaskedRMSE]\n",
    "    )\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "9e4186f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "data_dir = \"./\"\n",
    "\n",
    "# checkpoint\n",
    "checkpoint = ModelCheckpoint(\n",
    "    data_dir+\"best_model_ae-lstm.h5\",\n",
    "    monitor='val_loss',\n",
    "    verbose=1,\n",
    "    save_best_only=True,\n",
    "    mode='min'\n",
    ")\n",
    "es = EarlyStopping(\n",
    "    patience=20,\n",
    "    verbose=1,\n",
    "    monitor='val_loss'\n",
    ")\n",
    "\n",
    "callbacks_list = [es, checkpoint]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "090df6d7",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "d4d0f8d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train the model\n",
    "\n",
    "autoencoder = get_model(\n",
    "    data[\"train\"][\"x_train\"],\n",
    "    int(max_input_length),\n",
    "    int(max_output_length)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "4dddaf72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " masking (Masking)           (None, 288, 1)            0         \n",
      "                                                                 \n",
      " encoder-1 (LSTM)            (None, 288, 64)           16896     \n",
      "                                                                 \n",
      " encoder-2 (LSTM)            (None, 64)                33024     \n",
      "                                                                 \n",
      " repeat-vector (RepeatVector  (None, 39, 64)           0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " decoder-1 (LSTM)            (None, 39, 64)            33024     \n",
      "                                                                 \n",
      " decoder-2 (LSTM)            (None, 39, 64)            33024     \n",
      "                                                                 \n",
      " dense-1 (Dense)             (None, 39, 128)           8320      \n",
      "                                                                 \n",
      " dense-2 (Dense)             (None, 39, 16)            2064      \n",
      "                                                                 \n",
      " output (Dense)              (None, 39, 1)             17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 126,369\n",
      "Trainable params: 126,369\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b026fac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    autoencoder.load_weights(data_dir + \"best_model_ae-lstm.h5\")\n",
    "except:\n",
    "    print(\"No model weights yet. Training from scratch.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "29a03230",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "72/72 [==============================] - ETA: 0s - loss: 0.0470 - MaskedMAPE: 6.8512 - MaskedRMSE: 0.0470\n",
      "Epoch 00001: val_loss improved from inf to 0.03390, saving model to ./best_model_ae-lstm.h5\n",
      "72/72 [==============================] - 38s 438ms/step - loss: 0.0470 - MaskedMAPE: 6.8512 - MaskedRMSE: 0.0470 - val_loss: 0.0339 - val_MaskedMAPE: 6.0768 - val_MaskedRMSE: 0.0339\n",
      "Epoch 2/10\n",
      "72/72 [==============================] - ETA: 0s - loss: 0.0309 - MaskedMAPE: 4.4392 - MaskedRMSE: 0.0309\n",
      "Epoch 00002: val_loss did not improve from 0.03390\n",
      "72/72 [==============================] - 31s 429ms/step - loss: 0.0309 - MaskedMAPE: 4.4392 - MaskedRMSE: 0.0309 - val_loss: 0.0359 - val_MaskedMAPE: 5.4207 - val_MaskedRMSE: 0.0359\n",
      "Epoch 3/10\n",
      "72/72 [==============================] - ETA: 0s - loss: 0.0271 - MaskedMAPE: 3.7552 - MaskedRMSE: 0.0271\n",
      "Epoch 00003: val_loss improved from 0.03390 to 0.02269, saving model to ./best_model_ae-lstm.h5\n",
      "72/72 [==============================] - 31s 431ms/step - loss: 0.0271 - MaskedMAPE: 3.7552 - MaskedRMSE: 0.0271 - val_loss: 0.0227 - val_MaskedMAPE: 3.5304 - val_MaskedRMSE: 0.0227\n",
      "Epoch 4/10\n",
      "72/72 [==============================] - ETA: 0s - loss: 0.0286 - MaskedMAPE: 3.9750 - MaskedRMSE: 0.0286\n",
      "Epoch 00004: val_loss did not improve from 0.02269\n",
      "72/72 [==============================] - 30s 420ms/step - loss: 0.0286 - MaskedMAPE: 3.9750 - MaskedRMSE: 0.0286 - val_loss: 0.0370 - val_MaskedMAPE: 5.8811 - val_MaskedRMSE: 0.0370\n",
      "Epoch 5/10\n",
      "72/72 [==============================] - ETA: 0s - loss: 0.0275 - MaskedMAPE: 3.8186 - MaskedRMSE: 0.0275\n",
      "Epoch 00005: val_loss did not improve from 0.02269\n",
      "72/72 [==============================] - 30s 416ms/step - loss: 0.0275 - MaskedMAPE: 3.8186 - MaskedRMSE: 0.0275 - val_loss: 0.0268 - val_MaskedMAPE: 4.3127 - val_MaskedRMSE: 0.0268\n",
      "Epoch 6/10\n",
      "72/72 [==============================] - ETA: 0s - loss: 0.0261 - MaskedMAPE: 3.6039 - MaskedRMSE: 0.0261\n",
      "Epoch 00006: val_loss improved from 0.02269 to 0.01943, saving model to ./best_model_ae-lstm.h5\n",
      "72/72 [==============================] - 32s 442ms/step - loss: 0.0261 - MaskedMAPE: 3.6039 - MaskedRMSE: 0.0261 - val_loss: 0.0194 - val_MaskedMAPE: 2.9892 - val_MaskedRMSE: 0.0194\n",
      "Epoch 7/10\n",
      "72/72 [==============================] - ETA: 0s - loss: 0.0327 - MaskedMAPE: 4.6340 - MaskedRMSE: 0.0327\n",
      "Epoch 00007: val_loss did not improve from 0.01943\n",
      "72/72 [==============================] - 32s 448ms/step - loss: 0.0327 - MaskedMAPE: 4.6340 - MaskedRMSE: 0.0327 - val_loss: 0.0235 - val_MaskedMAPE: 3.5780 - val_MaskedRMSE: 0.0235\n",
      "Epoch 8/10\n",
      "72/72 [==============================] - ETA: 0s - loss: 0.0272 - MaskedMAPE: 3.7680 - MaskedRMSE: 0.0272\n",
      "Epoch 00008: val_loss did not improve from 0.01943\n",
      "72/72 [==============================] - 30s 423ms/step - loss: 0.0272 - MaskedMAPE: 3.7680 - MaskedRMSE: 0.0272 - val_loss: 0.0281 - val_MaskedMAPE: 4.5730 - val_MaskedRMSE: 0.0281\n",
      "Epoch 9/10\n",
      "72/72 [==============================] - ETA: 0s - loss: 0.0269 - MaskedMAPE: 3.7126 - MaskedRMSE: 0.0269\n",
      "Epoch 00009: val_loss did not improve from 0.01943\n",
      "72/72 [==============================] - 32s 439ms/step - loss: 0.0269 - MaskedMAPE: 3.7126 - MaskedRMSE: 0.0269 - val_loss: 0.0200 - val_MaskedMAPE: 3.2070 - val_MaskedRMSE: 0.0200\n",
      "Epoch 10/10\n",
      "72/72 [==============================] - ETA: 0s - loss: 0.0273 - MaskedMAPE: 3.7692 - MaskedRMSE: 0.0273\n",
      "Epoch 00010: val_loss improved from 0.01943 to 0.01495, saving model to ./best_model_ae-lstm.h5\n",
      "72/72 [==============================] - 32s 448ms/step - loss: 0.0273 - MaskedMAPE: 3.7692 - MaskedRMSE: 0.0273 - val_loss: 0.0150 - val_MaskedMAPE: 2.2997 - val_MaskedRMSE: 0.0150\n"
     ]
    }
   ],
   "source": [
    "epochs_num = 10\n",
    "history = autoencoder.fit(\n",
    "    data[\"train\"][\"x_train\"],\n",
    "    data[\"train\"][\"y_train\"],\n",
    "    batch_size=128,\n",
    "    epochs=epochs_num,\n",
    "    validation_data=(data[\"val\"][\"x_val\"],data[\"val\"][\"y_val\"]),\n",
    "    verbose=1, \n",
    "    callbacks=callbacks_list\n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "2e2c4092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': [0.047044314444065094,\n",
       "  0.030940350145101547,\n",
       "  0.027057861909270287,\n",
       "  0.028628021478652954,\n",
       "  0.02746533416211605,\n",
       "  0.026120752096176147,\n",
       "  0.03270426392555237,\n",
       "  0.027194039896130562,\n",
       "  0.02688552998006344,\n",
       "  0.027298660948872566],\n",
       " 'MaskedMAPE': [6.851233959197998,\n",
       "  4.4392499923706055,\n",
       "  3.755216360092163,\n",
       "  3.9750051498413086,\n",
       "  3.8185925483703613,\n",
       "  3.6039021015167236,\n",
       "  4.634044170379639,\n",
       "  3.7680466175079346,\n",
       "  3.712634563446045,\n",
       "  3.7692031860351562],\n",
       " 'MaskedRMSE': [0.047044310718774796,\n",
       "  0.030940363183617592,\n",
       "  0.027057860046625137,\n",
       "  0.028628021478652954,\n",
       "  0.0274653397500515,\n",
       "  0.026120755821466446,\n",
       "  0.03270426765084267,\n",
       "  0.02719404362142086,\n",
       "  0.026885541155934334,\n",
       "  0.027298666536808014],\n",
       " 'val_loss': [0.03390471264719963,\n",
       "  0.03587475046515465,\n",
       "  0.022686520591378212,\n",
       "  0.03699927404522896,\n",
       "  0.026802798733115196,\n",
       "  0.019431736320257187,\n",
       "  0.023483073338866234,\n",
       "  0.028095073997974396,\n",
       "  0.019964011386036873,\n",
       "  0.014952301979064941],\n",
       " 'val_MaskedMAPE': [6.076828479766846,\n",
       "  5.420684337615967,\n",
       "  3.530449390411377,\n",
       "  5.881130218505859,\n",
       "  4.312693119049072,\n",
       "  2.989215850830078,\n",
       "  3.5780398845672607,\n",
       "  4.573001384735107,\n",
       "  3.2069575786590576,\n",
       "  2.2997186183929443],\n",
       " 'val_MaskedRMSE': [0.03390471637248993,\n",
       "  0.03587476164102554,\n",
       "  0.02268652804195881,\n",
       "  0.036999281495809555,\n",
       "  0.026802802458405495,\n",
       "  0.019431743770837784,\n",
       "  0.02348308078944683,\n",
       "  0.028095083311200142,\n",
       "  0.01996401511132717,\n",
       "  0.014952307567000389]}"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "5ba5d434",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./best_model_ae-lstm.h5\n"
     ]
    }
   ],
   "source": [
    "# load weights from best model\n",
    "print(data_dir + \"best_model_ae-lstm.h5\")\n",
    "autoencoder.load_weights(data_dir + \"best_model_ae-lstm.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "a728c065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u3TpWrVpFt27d/na8QYMGB7++/vrrGTRoEE899VSFkuyhQ4fyyiuvHPx67969vPHGG0qyxbd2puXw7I/raRoewuSRHd0OR0REREREynHf5ytZtW2/22HQq21j7jmjt8/7HT169N8SbOCQBHvfvn0UFBQwatQo3n///Qr1e/XVVx/y9ahRo/jkk0/Yv38/jRs3PrygS6E12fVQ3+hI+kVHMi1+M9Zat8MREREREZF6LjY2tsTjX3zxBcOHDycsLIymTZvSokULXnrpJdLS0irUb0xMzCFfN2nSBHAS9uqikex6auKwGP758XIWbtrHkI5N3Q5HRERERETKUB2jxzVJ0RHrQr/99htnnnkmRx11FC+++CJt2rQhODiYt956i2nTplWo38DAwBKPV+dgo5LseuqM/m3575ermRa/WUm2iIiIiIhUO2NMpa7/6KOPCAsL49tvvyU0NPTg8bfeesvXofmUpovXU+GhQYwd2I4vlm9nX2au2+GIiIiIiEgdFx4eDkBqamqFrg8MDMQYQ0FBwcFjSUlJfPrpp9UQne8oya7HJsbFkJvv4aNFyW6HIiIiIiIiddzgwYMBuOuuu3j33XeZPn06mZmZpV5/2mmnkZWVxcknn8zLL7/M/fffT1xcHF26dPFXyFWiJLse69mmMYNiopiWoAJoIiIiIiJSvQYOHMhDDz3E0qVLufjii5kwYQK7d+8u9frRo0fzxhtvsGPHDm666Sbef/99Hn30UcaOHevHqCvPKLnyPWNMRyAxMTHxb/u/1TQfLkzmtplLef+K4Yzo3MztcERERERE6r3Vq1fTs2dPt8Oo18r7O0hKSiqsiB5rrU0qek4j2fXc6f3a0DgsiGkJm90ORUREREREpNZTkl3PhQUHMm5wNN+s2M6ejBy3wxEREREREanVlGQLk+JiyCuwfLhQBdBEREREREQOh5JsoUvLCIbFNmVa/GY8Hq3RFxERERERqSol2QI4o9mbU7KYs3GP26GIiIiIiIjUWkqyBYCT+7SmaXgIU+epAJqIiIiIiEhVKckWAEKDAjlncDTfr97Jrv3ZbocjIiIiIiJSKynJloMmDIuhwGP5YMEWt0MRERERERGplZRky0GxzcM5oksz3k/YQoEKoImIiIiIiFSakmw5xKS4DmxNPcCv63a7HYqIiIiIiEitoyRbDnFCr1Y0bxTK1PhNbociIiIiIiJS6yjJlkMEBwZw3pBoflqzi22pB9wOR0REREREpFZRki1/M2FYDBaYPl8F0ERERERExDfmzZvHvffeS2pqarX0v2PHDu69916WLFlSLf1XlJJs+Zv2TRtyVNcWzJi/mfwCj9vhiIiIiIhIHTBv3jzuu+++ak2y77vvPiXZUjNNioth5/4cflyzy+1QREREREREag1Xk2xjTKgx5lFjzDZjzAFjzDxjzHEVbNvOGPOBMSbVGLPfGPOpMSa2nDZxxhiPMcYaY6KKnbvXe7z4Y8dhvMRaa3SPlrRuHMa0+M1uhyIiIiIiIrXcvffey8033wxAbGwsxhiMMSQlJQHw1ltvMWjQIBo0aEDz5s2ZPHkyO3fuPKSPBQsWcNJJJ9G8eXMaNGhAbGwsl156KQCzZ89m4MCBAFxyySUH+3/77bf99hoLBfn9jod6GxgHPANsAC4GvjbGHG2tnVtaI2NMI+BnIAJ4EMgHbgZmG2MGWGv3ldDGAM8BWUB4GTFd5b2mUL2s/hUUGMD5Q9vz3E/r2ZKSRfumDd0OSUREREREaqmzzz6bjRs38t577/H000/TvHlzAFq0aMF9993H/fffz4QJE7jyyivZvn07zz77LPPnz2fhwoU0aNCAXbt2ceKJJxIbG8vdd99NeHg4iYmJfPLJJwD07NmTBx98kLvuuosrr7ySUaNGATBy5Ei/v1bXkmxjzDBgPHCztfYZ77EpwArgUeCoMppfC3QBBltrF3vbfu1tezPwnxLaTPa2eQO4oYy+P7DWplbmtdRV44e15/mf1vN+wmbuOLmH2+GIiIiIiEgt1a9fPwYPHsx7773HmDFj6NixIwBJSUk88MADPPbYY9x6660Hrz/llFMYOXIk77zzDldffTV//PEH+/btY+3atbRo0eLgdQ899BAArVq14tRTT+Wuu+5ixIgRXHDBBX59fUW5OZJ9DpAHvF54wFqbbYx5A3jQGNPGWru9jLbzChNsb9s1xpgfgfMolmQbYyKAh4F7gWblxGWMMY2BdGutreRrqlPaRDZgdI9WfLAgmZuO70ZIkJbwi4iIiIi45q3TSj5+yZfO89f/hB3L/37+5IehTT9YPBWWTPv7+QETYeAk2L4MvvnX38+37gunPFL1uMvwySefYK3l7LPPZs+ePQePd+nShTZt2jB79myuvvpqoqKiDl5/+eWXExBQc3MTNyMbCKyx1mYUO54AGGBASY2MMQFAP2BBCacTgG7GmOJzm/8NpAEvVSCuzd5r04wxbxpjmlagTZ01KS6GPRk5fL9qZ/kXi4iIiIiIVML69evxeDx06tSJFi1aHPLYtm0bu3fvBuDoo49m3LhxXHXVVbRo0YJzzz2Xd999l9zcXJdfwd+5OZLdBthawvHC0eu2pbRrCoQWua54W+PteyOAMaYrcCMwzlqb7yzNLtE+4HlgHpALjMZZnz3IGBNnrc0pqZG3gFpUscPRpd2ktjmqWwvaRTVgWsImTuvXxu1wRERERETqr8IR69KUN9o8cJLzKE2bfuXfw8c8Hg+BgYF8/fXXlJSrNWnSBABjDB9++CHx8fF8/vnnfPvtt1x00UU88cQTzJkzh0aNGvk17rK4mWQ3AEpKXLOLnC+tHZVo+zTwq7X2i7KCsdY+W+zQh8aYFcALwEXAa6U0vQm4p6y+a7PAAMOEYe154rt1JO7JJLZ5WTXjRERERERESlZSEt25c2cKCgro2rXrwXXaZYmLiyMuLo7//ve/fPDBB5x//vnMmDGDyy67rMT+3eDmdPEDOCPSxYUVOV9aOyrS1hhzMnAycEsVY3wZp9J4WduKPQPEFnuMquL9aqTzhrQnKMDwfoK28xIRERERkaoJD3cG7FJTUw8eGzt2LAEBAdx///1/u97j8ZCSkgLAvn37KF4ya8CAAQBkZ2eX2r8b3BzJ3o4zrbu4wmPbSmmXgjOKXVpby19TyR8DZgHpxpiO3mNR3ucYY0yDMoqrYa31GGO24kxRL+2aVCC16LGa8gmKr7RsHMYJvVoxc8EWbjmhG2HBgW6HJCIiIiIitczgwYMBuOuuuxg/fjzBwcGcccYZ3H///dx9991s3LiRM844g/DwcDZu3MhHH33EXXfdxeWXX84777zDiy++yJgxY+jcuTOZmZm8/vrrNG7cmFNPPRWAjh070rRpU15++WUiIiIIDw8nLi6O2NhYv75ON5PsJcCNxphGxYqfxXmfl5bUyJv4LgeGlHA6DlhvrS3c5zoG6AuMLeHapUA8MLy0AI0xwUB7YH4Zr6NemBgXw9crdvDtyh2cNaCd2+GIiIiIiEgtM3DgQB566CFeeOEFvvnmGzweD4mJidx111107dqVZ555hnvuuYeAgABiYmIYM2YMJ5xwAuAUPktISGDGjBns3LmTyMhIhg0bxrvvvnswiQ4KCmLKlCnceeedXH311eTn5/PWW2/5Pck2bu1SZYyJwykyVnSf7FCcva53WmuP9B6LARpaa9cUaXsnzpZcRffJ7g6sBB6x1t7tPXYaEFzs1uOB84ELgC3W2l+917aw1u4uFuP/AQ8Ck621Uyrx2joCiYmJiRVaV1AbeDyWY5+cTavGYXxw1Qi3wxERERERqbNWr15Nz5493Q6jXivv7yApKakweY+11iYVPefaSLa1Nt4YMxN4zBhTWA18MtABuLjIpVOAo3Gqhhd6EbgC+MoY8ySQj7PuejtOobPCe/ytNJ4xZoD3j196p3oX2mSMmY6T5OcAxwLjgN+BEjaTq18CAgwThsXwyNdrWL8zna6tItwOSUREREREpMZxewfvi4Bnvc/P4Yw6n2qtnVNWI2ttOnAMTgL8b+ABnOnnR1tr91Yxlqk4U8fvA57CmWb+AHCitTa/in3WKecOjiY40DBNBdBERERERERK5OaabKy12cDt3kdp1xxTyvFk4Nwq3PNe4N4Sjl9R2b7qm2aNQjm5Txs+WpjMnSf3UAE0ERERERGRYtweyZZaZuKwGPZn5/PFslKLsouIiIiIiNRbSrKlUoZ3akqnFuFMjd/kdigiIiIiIiI1jpJsqRRjDBOHxbB4cyqrtu13OxwREREREZEaRUm2VNo5g6MJCQpgWoJGs0VEREREqoNbWy3L4b/3SrKl0qIahnB63zZ8ungbmTkqvC4iIiIi4ktBQUHk5+v3bLfk5+cTFFT1GuFKsqVKJg2PISMnn8+XbnM7FBERERGROiUsLIyMjAy3w6i30tPTCQsLq3J7JdlSJYNimtC9VQRT47VntoiIiIiIL7Vo0YLdu3eTlZWlaeN+ZK0lKyuLPXv20KJFiyr34+o+2VJ7GWOYNDyG/3y2kmXJqfSLjnI7JBERERGROiEsLIxWrVqxY8cOcnJy3A6nXgkNDaVVq1aHNZKtJFuqbMzAdjz81RqmxW9Wki0iIiIi4kORkZFERka6HYZUgaaLS5U1DgvmjP5tmLV0G/uz89wOR0RERERExHVKsuWwTIrrQFZuAZ8t3up2KCIiIiIiIq5Tki2HpV90JL3bNmZq/GYVZRARERERkXpPSbYcFmMMk+I6sGZHOos2p7odjoiIiIiIiKuUZMthO3NAW8JDApmm7bxERERERKSeU5Ith61RaBBjBrbji2XbSMtSATQREREREam/lGSLT0yMiyEn38NHi5LdDkVERERERMQ1SrLFJ3q3jWRA+yimJagAmoiIiIiI1F9KssVnJsbFsGFXBgmJKW6HIiIiIiIi4gol2eIzZ/RrS0RYENMSVABNRERERETqJyXZ4jMNQgIZNyiar5fvICUz1+1wRERERERE/E5JtvjUxLgYcgs8fLhwi9uhiIiIiIiI+J2SbPGpbq0iGNqxCdPiN+PxqACaiIiIiIjUL0qyxecmxsWQtDeLuX/udTsUERERERERv1KSLT53Sp82RDUMZlq8CqCJiIiIiEj9oiRbfC4sOJBzBkXz7cod7ErPdjscERERERERv1GSLdViQlwM+R7LzAXJbociIiIiIiLiN0qypVp0btGIEZ2a8X6CCqCJiIiIiEj9oSRbqs3EuBiS9x3g1/W73Q5FRERERETEL5RkS7U5qXdrmoWHMFUF0EREREREpJ5Qki3VJiQogHOHtOenNbvYnnbA7XBERERERESqnZJsqVYTh8VQ4LHMmL/F7VBERERERESqnZJsqVYxzRoyqmtzZszfQn6Bx+1wREREREREqpWSbKl2k+I6sD0tm9lrVQBNRERERETqNiXZUu2O69mSlhGhTI3f5HYoIiIiIiIi1UpJtlS74MAAzh/antnrdpO8L8vtcERERERERKqNkmzxi/HDYjDA9AQVQBMRERERkbpLSbb4RbuoBhzTvSUzFmwhTwXQRERERESkjlKSLX4zKS6G3ek5/LBqp9uhiIiIiIiIVAsl2eI3x3RvSdvIMKYlbHY7FBERERERkWqhJFv8JjDAMH5YDL+t30PSnky3wxEREREREfE5JdniV+cPbU9ggOH9+RrNFhERERGRukdJtvhVq8ZhHN+zJR8uSCYnv8DtcERERERERHxKSbb43cS4DuzNzOXblSqAJiIiIiIidYuSbPG7UV2a075pA6bFb3I7FBEREREREZ9Ski1+FxBgmDAshnl/prBhV4bb4YiIiIiIiPiMkmxxxbmD2xMUYHhf23mJiIiIiEgdoiRbXNEiIpST+rTmw4XJZOepAJqIiIiIiNQNSrLFNZOGxZB2II+vlm93OxQRERERERGfUJItrhnRuRmdmoczNV5TxkVEREREpG5Qki2uMcYpgLZw0z7W7kh3OxwREREREZHDpiRbXDVucDQhQQHazktEREREROoEJdniqqbhIZzapzUfL9pKVm6+2+GIiIiIiIgcFiXZ4rqJcR1Iz8nni6UqgCYiIiIiIrWbkmxx3dCOTejashFTNWVcRERERERqOSXZ4jpjDBPjYlianMaKrWluhyMiIiIiIlJlribZxphQY8yjxphtxpgDxph5xpjjKti2nTHmA2NMqjFmvzHmU2NMbDlt4owxHmOMNcZE+aJP8Y2zB0YTFhyg7bxERERERKRWc3sk+23gZuA94EbAA3xtjBlRViNjTCPgZ2AU8CBwDzAImG2MaVJKGwM8B2T5qk/xnciGwZzery2zlmwlI0cF0EREREREpHZyLck2xgwDxgN3WGvvsNa+CowGNgOPltP8WqALcKq19nFr7dPAiUA7nKS9JJO9bd7wYZ/iQ5PiYsjMLeDTxVvdDkVERERERKRK3BzJPgfIA14vPGCtzcZJgo80xrQpp+08a+3iIm3XAD8C5xW/2BgTATwM3Avs80Wf4nsD2kfRs01jpsVvxlrrdjgiIiIiIiKV5maSPRBYY63NKHY8ATDAgJIaGWMCgH7AghJOJwDdjDENix3/N5AGvOTDPsXHjDFMioth1fb9LE1WATQREREREal93Eyy2wAlbYxceKxtKe2aAqFltDXevgEwxnTFWe99m7W2tMW+leqzKGNMlDGmY9EHEF3KfaQcZw1oS8OQQKbO03ZeIiIiIiJS+7iZZDcAcko4nl3kfGntqETbp4FfrbVflBNLZfos6iYgsdjjtzLuJWWICAvmrAFt+XzZNtIO5LkdjoiIiIiISKW4mWQfwBk9Li6syPnS2lGRtsaYk4GTgVsqEEuF+izBM0Bssceocu4nZZg4rAPZeR4+WZTsdigiIiIiIiKV4maSvZ2Sp2AXHttWSrsUnBHn0tpa/pr2/RgwC0gvMpU7ynsupkhxtcr0eQhrbaq1NqnoA1B2eBj6RkfSLzqSaQkqgCYiIiIiIrWLm0n2EqCHd3/qouK8z0tLamSt9QDLgSElnI4D1ltrC/fCjgHGcuhU7huL9P9JFfoUP5gUF8O6nRks2FRaMXgREREREZGax80k+0MgGLi88IAxJhS4BJhjrd3mPRZjjOlRQtvhxpiBRdp2x9lne2aR6ybhJNlFHzO85y4A7qhCn+IHZ/RvS0RoENPiN7sdioiIiIiISIUFuXVja228MWYm8Jh32vZGYDLQAbi4yKVTgKNxKnwXehG4AvjKGPMkkI+z7no7TqGzwnt8Wfy+xpgB3j9+aa1NrWyf4h8NQ4IYO6gd0+dv4T+n96JJeIjbIYmIiIiIiJTLzZFsgIuAZ73Pz+GMbJ9qrZ1TViNrbTpwDPA7zh7YD+BMPz/aWru3KoFUR59yeCbGxZCb7+EjFUATEREREZFawqiwlO95C6wlJiYm0rFjR5ejqd3GvfQH+zJz+fHWozHGlN9ARERERESkmiUlJREbGwsQ6y1+fZDbI9kiZZo4LIY/92Qy909NJhARERERkZpPSbbUaKf1a0Nkg2AVQBMRERERkVpBSbbUaGHBgYwbFM23K3ewJyPH7XBERERERETKpCRbaryJce3JK7DMXKACaCIiIiIiUrMpyZYar0vLCOJim/J+wmY8HhXqExERERGRmktJttQKE+Ni2JySxe8b9rgdioiIiIiISKmUZEutcHKf1jQND1EBNBERERERqdGUZEutEBoUyLmDo/l+9U527s92OxwREREREZESKcmWWmPCsBgKPJYP5m9xOxQREREREZESKcmWWqNj83CO7NKc9xM2U6ACaCIiIiIiUgMpyZZaZWJcDNvSsvll3S63QxEREREREfkbJdlSq5zQqxUtIkKZOk8F0EREREREpOZRki21SnBgAOcNiebntbvYmnrA7XBEREREREQOoSRbap3xQ2OwwIwEjWaLiIiIiEjNoiRbap32TRtydLcWzFiwhfwCj9vhiIiIiIiIHKQkW2qlSXEd2Lk/hx/XqACaiIiIiIjUHEqypVY6tnsLWjcOY2q8poyLiIiIiEjNoSRbaqWgwADOH9qe39bvZvPeLLfDERERERERAZRkSy02flh7DPD+fI1mi4iIiIhIzaAkW2qtNpENGN2jFTMXbCE3XwXQRERERETEfUqypVabNDyGPRm5fLdqh9uhiIiIiIiIKMmW2u2ori1oF9WAaSqAJiIiIiIiNYCSbKnVAgMME+Ni+GPjXv7cneF2OCIiIiIiUs8pyZZa79wh0QQFGN5P0Gi2iIiIiIi4S0m21HotI8I4sXcrPlyYTHZegdvhiIiIiIhIPaYkW+qEicM6sC8rj29WqACaiIiIiIi4R0m21AkjOzejQ7OGKoAmIiIiIiKuUpItdUJAgGHisBgSklJYtzPd7XBERERERKSeUpItdcY5g6MJCQzQaLaIiIiIiLhGSbbUGc0ahXJyn9Z8tCiZA7kqgCYiIiIiIv6nJFvqlIlxMaRn5/PFsm1uhyIiIiIiIvWQkmypU+Jim9K5RThTNWVcRERERERcoCRb6hRjDBPjOrBkSyqrtu13OxyRuiXvABxIdTsKERERkRpNSbbUOeMGtSM0KIBpCZvcDkWkbnnnDHi0gxJtERERkTIoyZY6J6phCKf1a8Oni7eRmZPvdjgidcO2xZA83/nz1gXuxiIiIiJSgynJljppUlwMGTn5zFqqAmgiPvHH8xASATetgC7Hux2NiIiISI2lJFvqpEExTejROoKp8ZoyLnLY9m2ClZ/CkIshqj3kZTtJd16225GJiIiI1DhKsqVOcgqgxbBi636WJae6HY5I7WYLoNeZEHe183VyAnx3N/z2hLtxiYiIiNRASrKlzhozsB0NggOZOk/beYkclqad4Ny3ITLa+Tr2KOg3Hn5/GnaucjU0ERERkZpGSbbUWY3Dgjmzf1tmLd3G/uw8t8MRqZ2WzYTVX/z9+EkPQVgkzPoHeAr8H5eIiIhIDaUkW+q0ScNjOJBXwKeLt7odikjtk58D390FC974+7nwZnDyI06l8fmv+z82ERERkRpKSbbUaf2io+jTrjHT4jdjrXU7HJHaZflMyNgJI/9R8vm+5zqVxgu39hIRERERJdlS902K68CaHeks2pzqdigitYfH41QQb9UXOh1b8jXGwHnvwtmv+Tc2ERERkRpMSbbUeWf2b0uj0CBt5yVSGRt+gN1rnFFsY0q/LqShc379D7D2a//FJyIiIlJDKcmWOi88NIizBrTly2XbSc3KdTsckdph6TSIaAt9zi7/Wo8Hfv6vUwQtK6X6YxMRERGpwZRkS70wKa4DOfkePlqkAmgiFTL2VbjwEwgMLv/agAA483knwf7u39Ufm4iIiEgNpiRb6oVebRszoH0U0+I3qQCaSHmy0yAoBFr2qHib1n3hiBtgyXvw5y/VF5uIiIhIDackW+qNSXExbNydSXyiprOKlGrfJniiO6z4qPJtj74TmnaCz2+EvAO+j01ERESkFlCSLfXG6f3aEhEWxLT4zdV/s5x0WPUZfHkb7FxZ/fcT8ZV5L4EnD9oPr3zb4AZwxrPgyYd9ST4PTURERKQ2CHI7ABF/aRASyLhB0UyN38TejF40axRaPTf65l8w/3Uo8BZZ270GLv6ieu4l4ksH9sGiKc7+15HtqtZH7FHwj0XOdHMRERGRekgj2VKvTIqLIa/A8uHC5MPvzOOBrYvg54fg5SNh91rnePOuMOxKmPwFnPhfSPoNkuYc/v1EqtuCNyEvE0Zcf3j9BIXA/m3w/X+gIN83sYmIiIjUEhrJlnqla6sIhnVsyvsJm7liVCcCAsrY/7c0m/6AZTNg3beQvh1MALSPc6aIAwy59K9ro4dAbia0qEQBKRE35OdA/CvQeTS07nP4/W2JhznPQnhLGHmYSbuIiIhILaKRbKl3JsbFkLQ3iz827q1Yg4xdzhTa1C3O1xt/huUfQvthMOZluG0DXPqNk1AXF9wAjvknhDfz3QsQqQ65mdD1BDjiJt/012sMdDsFfn5Q67NFRESkXjHazsj3jDEdgcTExEQ6duzocjRSXHZeASMe/pERnZvx4qTBf7/AWti1CtZ+7Ty2LgQsnPoEDLvCu71RGARVYk33gjdh01wY95rPXodIjZeWDC/EOR9IXfAxmCrMHBERERGpgZKSkoiNjQWItdYmFT2nkWypd8KCAzlncDTfrdzJrvRs52B+LuRmOX/+/Sl4aST89ABg4di74Oo5MPRybweRlUuwAbL3w/IPYMt8n70OEZ9JmgOLp0JBnm/7jYyG4+6BjT/Bsg9827eIiIhIDeVqkm2MCTXGPGqM2WaMOWCMmWeMOa6CbdsZYz4wxqQaY/YbYz41xsQWu6apMeYdY8xqY0y6MSbNGDPfGHOhMYcOqRhj7jXG2BIeO3z5mqVmmDAshnBPOku/fBVmXgyPd3amhAN0Pw3OeA5uXQtX/ARH3+6sUT2cUbihl0ODpvDrYz6JX8SnZj/sTOuuDkMvgw5HQPq26ulfREREpIZxu/DZ28A44BlgA3Ax8LUx5mhr7dzSGhljGgE/AxHAg0A+cDMw2xgzwFq7z3tpY6AT8AmwGQgEjgemAF2B/5TQ/VVAVpGvD1TtpUmNlTSHTj8/xKKwPwhc48E2aoXpPQbaDnTOt+zhPHwptJFT/OnH+52K5O0G+bZ/karausipgH/ifyEw2Pf9BwTC5M+dZxEREZF6wLUk2xgzDBgP3GytfcZ7bAqwAngUOKqM5tcCXYDB1trF3rZfe9vejDd59s6NH1Ws7QvGmFnATcaYe+zfF6V/YK1NrforkxqlIB+SE2DtV04S3Wec88t+diobu1/J7cvactP48zm2R+vqj2XoFTDnOfj1cZjwfvXfT6Qi/ngeQhvDoMnVd4+AQOffYvzL0KqXU8FcREREpI5yc7r4OUAe8HrhAWttNvAGcKQxpk05becVJtjetmuAH4HzKnDvTUA4UNKwjTHGNC4+nVxqkZx0WPkJfHwVPNEF3joF5r0MO1c652OGwzVz6Hjuw2wN78XUeB/smV0RYY1hxHVOfHnZ/rmnSFn2bYJVn8Lgi53vz+pkC2DRO/D5jU4lcxEREZE6ys0keyCwxlqbUex4AmCAASU1MsYEAP2ABSWcTgC6GWMaFmsTZoxpbozpaIy5CLgE+N1am1tCH5uBNCDNGPOmMaZpZV6UuCR1M+xc5fx52xJnnfX676DrSXDuO3DHn3DcoasDQoICOHdIe35as5PtaX5aFTDqVrj4CwgO88/9RMqydLqzz/vwa6r/XkGhTq2D1M3w80PVfz8RERERl7i5JrsNsLWE49u9z21LadcUCC1yXfG2xtv3xiLHLweeL/L1jzjrv4va571mHpALjMZZnz3IGBNnrc0pKRhjTBQQVexwdCmxi694PLBtMazzbrO1c4WTUE/6AGJGwCVfQ/QwCCz7W3zC0Bhe/mUjM+Zv4abju1V/3IXrUjf9AQ2bQws/3FOkNEfdDt1Ogsal/XfrYx1GwOBLYN6LztIN1SYQERGROsjNJLsBUFLiml3kfGntqGTbT4E1QHPgNJwk/JDRbmvts8XafGiMWQG8AFwElLbB8U3APaWcE1+y1qnwvXsdvHM6ZOwEE+gk1Sf+F7qf6lwXGAQdRlaoy5hmDRnVtQXTE7Zw/bFdCAr0w+SOnHSYdj50PhbOm1L99xMpSW4mhIRD2wH+ve8J9zkfjM26Aa78uXqKrYmIiIi4yM3p4gdwRqSLCytyvrR2VKattTbZWvuDtXa6tfZCYB3wgzGmtES+0Ms4lcbL2lbsGSC22KN4sTWpqvQdsPBtmDYePrzUOdakI3Q6Fs5+DW7fAJd8CSP/Ac06V+kWE4fFsGN/Nj+v3e2zsMsUGgFxV8Gqz/6a4i7iT/k58PwQ+PUJ/987LBJOe8JZA34g1f/3FxEREalmbo5kb8cZUS6u8Fhpm6qm4Ixil9bWUvJU8qI+BK7BqWD+bWkXWWs9xpitOFPUS7smFUgtekw10w5T9n6nCvHar2HbIudYVAz0Ptv5c1AInP2Kz253XM+WtGocytT4TZzQq5XP+i3T8Gth3ktOpfFz3/LPPUUKLfvA2be63WB37t/zDOhx+uHtPS8iIiJSQ7k5kr0E6OHd87qoOO/z0pIaWWs9wHJgSAmn44D11tqsEs4VVTiCHVnWRcaYYKA94KchznoqPwc2/OiMWINTIOmP5531y6P/DdfMhRuXOdNMq0FwYADnD2nPL+t2syWlvG8dH2nYFIZd6VRB373WP/cUAaeewR/PQ6u+0OkY9+IwBrbMh89vcpaCiIiIiNQRbibZH+JsoXV54QFjTChO5e851tpt3mMxxpgeJbQdbowZWKRtd5xiZTOLHGtRyr0vwxnxXlTOtbfjTEEvdbRbqihzLyx5Hz64CB7rBO+dDT89CJ4CJ8m+ZRVc/gMcdZuzr241j3idPywGA0yfv7la73OIEddDcEOY+z//3VNkw/ewZ62zxMLtkeSdy2HhW7D4PXfjEBEREfEh16aLW2vjjTEzgce8e2JvBCYDHTi08vcU4GicquGFXgSuAL4yxjwJ5AO34EwTf7rIddcZY8YAXwJJQBPgbJwR7xettRuKXLvJGDMdWIEzHf1YYBzwOzDt8F9xPWctZOyCiFZQkAfP9ofcdIhoA33Phe6nQOxRf1XfDo3wa3jtohpwbPeWzJifzE3HdyPYHwXQwpvBxOnQdmD514r4yh/PQ+N20OdstyOBQRfDspnw3d1OlfNGLd2OSEREROSwubkmG5yq3Q94n5sAy4BTrbVzympkrU03xhyDk1D/G2dE/mfgJmvt3iKX/gj09/bfEmdrruU4I9nFF8JOBY4AzgVCcJLyB4CHrbX5VX2B9VpBPmyZ56ytXvu1k2Tf8aezpvr0p6F5F2gzwP3RNK9Jw2P48e0FfL9qJ6f2LWnJfzWIPcp5zsvW3tlS/QryoXU/6D2mZlT1DgiAM56Fl4+Ar+9UfQIRERGpE4zVWjifM8Z0BBITExPp2LGjy9G4ZNY/YNUsyE6FwBAnmex+CgyYBMHlFXV3R4HHctRjPxPbPJz3Lo8rv4GvrP8eProcrvipyhXSRWq1Xx6Dnx+ECTOg+8luRyMiIiJSrqSkJGJjYwFirbVJRc+5uSZb6op9STDvZXh/gjMVHJy11T1Og/PedUavL/gIhl5eYxNsgMAAw/ih7fl9wx6S9mT678at+0J+Nvz+lP/uKfXPvk1ORftcP35vV9QRN0HHUeDRpCERERGp/dyeLi61VfJCWPMFrPsGdnn3em7eHfZvdfaxHvOiq+FV1XlD2/PMj+t5P2Ez/zq1p39uGtEaBl8M81+Ho2533j8RX5v3ovM91vNMCAl3O5pDBYXA5M9rzNIRERERkcOhkWypmNxMWPOls6YTYPZDMOdZaNgMTnoYblgM1yfU+gSxVeMwju/ZkpkLk8nJL/DfjY+4EUwA/KbRbKkGWSmw6F2nyGBkO7ejKZkxcCAVPrsOtiS4HY2IiIhIlWkkW0q3f5szUr32a/jzFyjIgUu+hg4j4ZTHnL2eGzRxO0qfmxTXgW9X7uSbFTs4a4CfEpLGbWHQZGev8KNug6gY/9xX6oeFb0FeprNtXE0WEAgbf3Zmylz1qzPCLSIiIlLLaCRb/mKt8wD48lZ4qid8cTPsWQdDL3Omc0YPdc4361wnE2yAI7s0J6ZpQ6bF+3HPbIAjb3K2MSpc1y7iC/k5EP8KdD4OWvdxO5qyhUbAaU/B7tXOTBkRERGRWkgj2fVdXjYk/eaMVq/7Bs55C2LiIPZoiIyGbqdAi+71aq1kQIBhwrAYHv1mDRt2pdOlpZ/27I6MhvFT/XMvqT/WfQMZO2HsK25HUjHdT4beY+HXx6DXWdCim9sRiYiIiFSKRrLrqw0/wowL4LFOMPUcWDod2g78a+/cXmfCkTdDyx71KsEudO6QaIIDDdPit/j/5ut/cKpAi/hCzzPh0u+g0zFuR1JxpzwGwQ3h8xvB43E7GhEREZFK0Uh2fbV7jbPusf946H4qdDwSgsPcjqrGaN4olJN6t+bDhVu44+TuhAUH+u/mqz+DpTOg1xho3MZ/95W6J++As21ejB/3ffeFRi3hpIdg6wIoyIUA/d8kIiIitYdGsuuroVfALavg9Keg6/FKsEswMS6G/dn5fLlsu39vPOpWZ79grUmVw/XeOfDJ1W5HUTUDJ8HpT+v/JhEREal1lGTXV0Eh9XIaeGWM6NSMTs3DmZbg5wJoTTpC/wlORej0nf69t9QdWxfCpt+hVW+3Izk8S6fDZzW8KrqIiIhIEUqyRUphjGFiXAwLN+1jzY79/r35qFucKuN/POff+0rd8cfzENrY2RquNtu/DRa/C6s/dzsSERERkQpRki1ShnGDogkJCvD/dl7NOkO/82DtV1CQ7997S+23LwlWfQaDL4awxm5Hc3hG/gNa9YUvb4PsNLejERERESmXkmyRMjQJD+G0vm34ZNFWsnL9nOye+CBcMxcCVZ9QKmneS2ACYPg1bkdy+AKD4cxnIXMX/HCv29GIiIiIlEtJtkg5JsbFkJ6Tz+dLt/n3xuHNnKJP+7fDgX3+vbfUbo3bwrCrnOe6oN1giLsGFrwJm+a6HY2IiIhImZRki5RjSIcmdGvViKn+njIOkLkXnhsIc7Q2WyrhiBvh5IfcjsK3Rt8F3U+DkHC3IxEREREpk5JskXIYY5g4LIZlyWksT/bzmtDwZtD9ZEh4FbJS/HtvqX3yc+D3Z+rm90pIOEyYBm36uR2JiIiISJmUZItUwNhB0YQFBzAtYZP/b37UHZCb4ayzFSnLsg/gh3tg+1K3I6k+KYnw1qmwa7XbkYiIiIiUSEm2SAVENgjmjH5t+WzJNtKz8/x781a9oOeZEP8yHEj1772l9vB4nG27WveFTse4HU31CY1wEuxZNzivWURERKSGUZItUkET42LIyi3gsyV+LoAGcPQdkLPfSbRFSrLhe9izFkbeAMa4HU31CW8OJz8MyQmw4A23oxERERH5GyXZIhU0oH0Uvdo0Zmr8Zqy1/r15675w0sPQe6x/7yu1x5znoHF0/fge6Xc+dB4NP9wHacluRyMiIiJyCCXZIhVkjGFiXAyrt+9nyZZU/wcw4lpo0d3/95Wab/da2PS7sy92YLDb0VQ/Y+D0p8EWwJe3gb8/9BIREREpg5JskUoYM7Ad4SGB7mznBZD0O7x5MuSku3N/qZladIcrZ8Ogi9yOxH+adIQTH4Aux7kdiYiIiMghlGSLVEKj0CDOHNCOL5ZtIy3LzwXQAIIbwOa5kPCa/+8tNVN+jjOS23YghDV2Oxr/Gno5DLvCGdnWaLaIiIjUEEqyRSppUlwM2XkePl7swlrQdoOhywlOFemcDP/fX2qe7/8Db54EngK3I3HP9/+BL25yOwoRKcHejBz++dEyFiSluB2KiIjfKMkWqaQ+7SLpHx3JNDcKoAEcfSccSFFlZYGsFFg0BZp2goBAt6NxkYGFb0Pir24HIiJFpGTmMun1eKbP38LE1+L5xI0Pp0VEXKAkW6QKJsV1YP2uDOYn7fP/zdsPdSorz3kOcrP8f3+pORa8CXlZMOJ6tyNx1zH/hCax8PmNkHfA7WhEBEjNyuWC1+NJ3JPJCxMHMahDFDfPWMqT363F49HyDhGp25Rki1TB6f3bEBEaxLT4Te4EcPSdzvrbVJfuL+7Ly4b4V6DzcdC6j9vRuCu4AZzxLKT8Cb885nY0IvVeWlYek16PZ8PuDF67aAin9WvDlEvjOG9INM//tIF/TF9Mdl49XuIiInWekmyRKmgYEsTYQe34asUOUjJz/R9AzHC4fgG07On/e0vNsPwDyNwFI//hdiQ1Q6ejYcAFMOdZ2LHc7WhE6q20A3lc8EY863dm8OqFgzmqWwsAQoICeHRcP/51Sg++Wr6d8a/OY1d6tsvRiohUDyXZIlU0MS6G3HwPHy10aY1ZQKAzcrfxZ3fuL+4KagDdT4VOx7gdSc1x4gPONmaNWrkdiUi9tD87j4veiGfNjv28fOEgjune8pDzxhiuOrozL18wmLU70hn7wh+s2bHfpWhFRKqPkmyRKurRujGDOzRhWoJLBdDAWYP66TXO1GGpX/qdCxPed7avEkfDpnDGM9CoZf2uti7igvTsPCa/mcCq7ft5adJgRvco/cOuk3q3ZubVI8j3eBj34h/8vGaXHyMVEal+SrJFDsOkuBgS92Qyd+NedwIYdRukb4fF77pzf3FHwmuQpiq9pdo0F/43FPapZoGIP2Tk5HPxW/NZnpzGCxMHcXyv8meT9GkXyWfXHUnH5uFc9s583p6T6IdIRUT8Q0m2yGE4tW8bIhsEMzVhszsBxB4F7YfD709Dfo47MYh/bV0IX90GKz91O5KaKzIaMnbCFzeDW7NMROqJzJx8LnkrgSVbUvnfxIGc2Lt1hdu2jgxj5tUjOL5nK+79fBX/+WwF+QWeaoxWRMQ/fJJkG2OCjDHjjDFXGGMq/r+rSC0XFhzIOYOj+XbFDnanu5DkGgPH3An7t8KSqf6/v/jfH89DaCQMnux2JDVXVHsY/W/Y+CMsn+l2NCJ1VlZuPpe8PZ9Fm1N5fsJATu7TptJ9NAwJ4uULBnPVUZ2YMncTl76zgP3ZedUQrYiI/1Q6yTbGPGaMmV/kawP8AHwAvAIsN8Z09l2IIjXbhGEx5HssMxducSeATsdC9FD4438atavr9iXBqs9gyMUQGuF2NDXbsCug3RD45p+Q6dJyDpE67EBuAZe+PZ8FSSk8c/4ATu1b+QS7UECA4V+n9uTRcX35Y8Mexr34B1tSsnwYrYiIf1VlJPtk4LciX58BHAU8Dkz0HvvnYcYlUmt0admI4Z2a8n7CZjweF5JcY+D0Z+Ciz1QEq66b+yKYQIi72u1Iar6AQDjzOchOg2//z+1oROqUA7kFXPbOfBISU3j6/AGc0b+tT/o9f2gMUy4bxq70HMa8MIeFm1J80q+IiL9VJcluD6wv8vUZQKK19p/W2unAy8BxvghOpLaYGNeBLSkH+G3DHncCaN3HmSKbnwsF+e7EINUrN9NZEtD3XGjsm19o67xWveGkh2HgJLcjEakzsvMKuGLKAub+uZcnz+vPWQPa+bT/kZ2b88m1I4kIC2LCa/F8tmSrT/sXEfGHqiTZIUDR3+KPxZkuXuhPoOpzhkRqoZN6t6JpeAjT4l2sZpy2FZ4fDMumuxeDVJ+QcLjyF2cNvlRc3JVOgUBroUDrPEUOR2GCPWfjHp44pz9jB0ZXy306tWjEJ9cewcD2Udw4fQlPf7/Ova0yRUSqoCpJ9hZgBIAxpjfQCfilyPmWQMbhhyZSe4QGBXLukGh+WL2Lnftd2rO6cVto2AR+fUKj2XVNQT54PNC8CzTp6HY0tU9BHkw9B3641+1IRGqtnPwCrn5vIb9v2MOj4/oxbnD1JNiFmoSH8O5lcZwzOJpnf1zPjdOXkJ1XUK33FBHxlaok2dOBycaYL4AvgP3AV0XODwQ2+iA2kVplwtAYCjyWGfNdKoBmDBx9J+xLhBUfuhODVI+l0+CFYZCxy+1IaqfAYGdbr3kvwrbFbkcjUuvk5BdwzXuLmL12N4+c3ZfzhrT3y31DggJ4/Jx+3HFyd2Yt3caE1+a5s5OHiEglVSXJfhh4G2c02wIXWWtTAYwxkcCZwI8+ik+k1ujYPJxRXZszPWEzBW4UQAPofiq06gu/Pg4efeJfJ3g8TuX44AYQ3sLtaGqv4++D8JYw6x+aNi5SCbn5Hq6buoif1uziobF9OX9ojF/vb4zh2mO68NKkQazevp8xL8xh7Y50v8YgIlJZlU6yrbU51trLrLXNrLWdrLWzipxOx1mPfa+vAhSpTSYOi2FbWjaz17o04mgMHH0H7N0AKz52JwbxrfXfwZ61MPIGVY8/HA2i4NTHYMdymPuC29GI1Aq5+R6um7aIH1bv4oExfZgY598Eu6hT+rbhg6tGkFfgYdxLf7j3c1ZEpAKqMpJdlmBrbZq1VsMEUi8d36sVLSJCmRq/2b0gepwOfcZBeHP3YhDf+eN5aBwNvce4HUnt1/NM59/H7IchJdHtaERqtLwCD/94fxHfr9rJ/Wf15sLhHdwOiX7RUXx2/RHENG3IpW/P550/ktwOSUSkRJVOso0xpxhj7i127FpjzH4g0xgzzRgT7KsARWqT4MAAzh/Snp/X7iJ5X5Y7QQQEwDlvQudj3bm/+M7WhbDpdxh+jbOuWA6PMXDq407tgsa+3XZIpC7JK/Bw4/TFfLtyJ/ec0YuLRnR0O6SD2kQ2YObVIxjdoyX3zFrJPZ+tIL/A43ZYIiKHqMpI9u1Aj8IvjDE9gWeBbcD3wPnAdT6JTqQWGj/MKQjjWgG0QrtWwzf/ctb0Su2UmwnRw2DQRW5HUnc0bgujboGgEMg74HY0IjVOfoGHm2Ys4avlO7j7tJ5cckSs2yH9TXhoEK9cOIQrRsXyztxNXD5lAenZmkQpIjVHVZLsnsCCIl+fDxwAhllrTwFmAJN9EJtIrRTdpCHHdGvBjPlbyHPz0/WdK51qyqtnlX+t1EyxR8Hl30NYY7cjqXuWzoBnB0DGbrcjEakx8gs83PLBUr5ctp3/O7UHl4/q5HZIpQoMMNx1Wi8ePrsvv6/fwzkvzWVLikszyEREiqlKkt0E2FPk6+OBn6y1+71fzwZq3seeIn40Ma4Du9Jz+HG1i4VZeo+FZl29lcY1ml3rrPgIdq9zO4q6q+0AOJAC3/zT7UhEaoQCj+W2mUuZtXQbd57cgyuP6ux2SBUyYVgM71w6jG1pBxj74hwWbd7ndkgiIlVKsvcAHQCMMRHAUOC3IueDgcDDD02k9jq2ewvaRIYxNX6Te0EEBMJRt8POFbD2q/Kvl5ojKwU+ux5+f8rtSOquFt1h1K3OnvLrvnM7GhFXFXgst3+4lE+XbOP2k7pzzTG1I8EudESX5nxy7RGEhwYx/tV5zFq6ze2QRKSeq0qSPRe42hhzDvAMEAR8XeR8F2D74YcmUnsFBQZw/tD2/LZ+D5v2ZroXSJ9x0LQT/PIoWJf27pbKW/Am5GXByH+4HUndduTN0KIHfHkL5GS4HY2IKzwey50fLePjRVu55YRuXHdsF7dDqpIuLRvxybVHMCA6ihveX8yzP6zH6ueeiLikKkn2Pd52HwCXAFOstasAjDEGGAvM8VmEIrXU+KExhAQGMOHVeXy1fLs7P+wDg2DUbc6+2Xs09bhWyMuG+Fegy/HQqrfb0dRtQaFwxnOQluxs6yVSz3g8ln99vJwPFyZz0/FdueG4rm6HdFiahofw7uXDOHtQO57+YR03z1hCdl6B22GJSD1U6STbm1D3BM4CjrHWXlLkdBTwNM4It0i91joyjGlXxNG4QTDXTl3EhW8ksGGXC6Nl/c6DG5c502Ol5lv+AWTu0ii2v8TEwelPwbAr3Y5ExK88Hstdn65gxoIt3DC6Czcd383tkHwiNCiQJ8/tz+0ndefTJduY9Ho8ezNy3A5LROoZo6k0vmeM6QgkJiYm0rFjR5ejEbflF3h4b94mnvx+HQdyC7jsyFj+cVxXGoUG+TeQ3CzI2AlNVZewRnvpSDDAVb85+zqL/+QdABPobO8lUodZa/n3Zyt4b95mrju2M7ed2B1TB/+/+Wr5dm6esYSWjUN5c/JQuraKcDskEalDkpKSiI2NBYi11iYVPVflJNsY0xlnNLtwf4c/gc+stRurHmrdoCRbSrI7PYdHv1nDhwuTadU4lLtO68UZ/dr47xeb10YDBi7/QclbTbZ/G2Tscqpfi/9kpTj/RgZOcgoGitRR1lrumbWSKXM3cfXRnbnz5LqZYBdauiWVy6csIDu3gBcmDeKobi3cDklE6oiykuyqrMnGGPMAsAZ4ArjW+3gCWGuMuf+wohWpo1pEhPLEuf356JqRtIgI5Yb3FzPxtXjW7Uz3TwADL4CtC2DjT/65n1ReQT40bqsE2w0Nm0Kb/vDL47Bng9vRiFQLay33fb6KKXM3ceVRnep8gg3Qv30Un153BO2aNOCSt+fz7jwXd/0QkXqj0km2MeZS4C4gHhgDdPU+xuBUHr/LGHOxzyIUqWMGd2jCZ9cdyX/H9GHV9v2c8uxvPPDFKtKz86r3xgMmQeNoVRqvqbYuhGf7wbbFbkdSf53yGASHwec3am95qXOstfz3y9W8/UcSlx0Zy79O6VHnE+xC7aIa8OE1IzmmWwv+/ekK7vt8JQUe/RwUkepTlZHs63AS7GOstbOstRu9j1nAsUACoIo9ImUIDDBcMLwDP992DOcNiebNOYmMfvIXPlmcXH1VyINC4cibYEs8JP5aPfeQqvvjeWcbqWa1c/ucOiGiFZz4X9j0Oyye4nY0Ij5jreXhr9fwxu+JXHJER+4+rWe9SbALNQoN4tWLhnDZkbG8NSeJy9+ZT0ZOvtthiUgdVZUkuycw3Vr7t/+ZvMeme68RkXI0DQ/h4bP78em1R9A2MoybZyzlvFfmsmrb/uq54cALIaKNM5otNce+JFj1GQy5GEJVmMdVAy+EjqPgu/8467RFajlrLY9+s5ZXf/2TySM68J/Te9W7BLtQYIDh36f34r9j+vDr+j2c89IfbE094HZYIlIHVSXJzgUalXE+wntNuYwxocaYR40x24wxB4wx84wxx1WwbTtjzAfGmFRjzH5jzKfGmNhi1zQ1xrxjjFltjEk3xqQZY+YbYy40JfyEqUifItWhf/soPrn2CB45uy8bdmVw+vO/ce+slaQd8PEU8uAwOPVxp7CTpozXHHNfdKpax13tdiRiDJzxLIx5wVmnLVKLWWt54ru1vPzLRi4YHsO9Z/autwl2URcM78Dblwxla+oBzvrfHBZv3ud2SCJSx1S6urgx5nugOzDUWruz2LmWwAJgtbX2pAr09T4wDmdf7Q3AxcAQ4Ghr7dwy2jUCFuEk9E8B+cDNgAUGWGv3ea/rCLwL/AZsBgKB43HWjz9grf1PZfusCFUXl8ORmpXLE9+tZWr8ZpqFh3DnyT0YNyiagAD9YlQnZaXA072h1xgY+5Lb0UhxmXshvJnbUYhUyVPfreW5nzYwYVgMD47po58jxWzYlc4lb89n1/4cnjyvP6f3a+t2SCJSi/h0Cy9jzFHAj0A68AawynuqN3AJTpJ6nLX2t3L6GYaztvtma+0z3mNhwApgm7X2qDLa3gE8Agy21i72HuvhbftQ0eS5lPazgGOASOt9Aw63z2L9d0RJthymFVvT+M9nK1i0OZVBMVHcf1Yf+rSL9E3nackw6x/OiHaHkb7pU6pm91r47Do44zlo1cvtaKSoHx+AJdPgungIa+x2NCKV8swP63jmh/WMH9qeh8b2VYJdir0ZOVz17kIWbNrHrSd04/rRXTTaLyIV4tMtvKy1vwJn4yTZt+Ik2m8At3iPjS0vwfY6B8gDXi/Sd7a3ryONMW3KaTuvMBn2tl2Dk/yfV4F7bwLCgWAf9iniU33aRfLh1SN5/Jx+bNqbxRn/+527PllOalaFVmOUrUFT2LEcfnns8PuSw9Oiu7N3uRLsmqf7qZC+HX68z+1IRCrl+R/X88wP6zl3cLQS7HI0axTK1CviGDuwHU9+v45bP1hKTn6B22GJSC1XpX2yrbWfA7FAHDDe+xgGdAKijTGrymheaCCwxlqbUex4AmCAASU1MsYEAP1wpqUXlwB0M8Y0LNYmzBjT3BjT0RhzEc6I++/W2tyq9iniDwEBhnOHtOen245h8oiOvJ+wmWOfmM37CZvxHM72IyENYeQN8OfPsCXBdwFL5fz5i7N1l9RM0YNh+DUw/3XYPM/taEQq5IWfN/Dk9+s4e1A7HhnXTwl2BYQGBfLUef259YRufLx4Kxe8Hk9Kpg8+0BaReqtKSTaAtdZjrZ1vrf3A+1hgrfUAzXHWbJenDbC9hOOFx0pbGNMUCC2jrfH2XdTlwG4gEXgHmAdMOsw+ATDGRHmT94MPILqU2EWqJLJBMPee2ZsvbxhFl5aN+NfHyxn74hyWbEmteqdDLoWGzVRp3C0eD3x1G3x+k4rQ1WTH3gWRMTDrBsjPcTsakTK9/MtGHv92LWMHtuPxc/oTqAS7wowx/OO4rvxv4kCWJacx5oU5bNiV7nZYIlJLVTnJ9oEGQEm/sWQXOV9aOyrZ9lPgBGAC8J73WNGR6ar0WegmnOS96KMi0+VFKq1nm8Z8cNUInj6/P9vSshn74hz++dGyqn3iHtoIRlwPG36AZI2m+t3672DPOmdGgdb/1VyhjeD0p2DPWoh/xe1oREr12q9/8sjXazizf1ueOFcJdlWd3q8t068cTlZuAWNf/IPf1+9xOyQRqYXcTLIP4IweFxdW5Hxp7ahMW2ttsrX2B2vtdGvthcA64AdjTINi11clnmdwps4XfYwq5VqRw2aMYezAaH669WguOyKWmQuTOfaJ2bw7bxMFlZ1CPuwKaNoJUpOqJVYpwx/PQWR76D3G7UikPF1PgLNfh6GXuR2JSIle/+1PHvxqNaf3a8NT5ynBPlwDY5rw6XUjaRfVgMlvJTA1fpPbIYlILeNmkr2dkqdgFx7bVkq7FJwR59LaWkqe9l3Uh0B7oLCCeZX7tNamWmuTij6A5HLuL3LYIsKCufv0Xnx94yh6tWnMvz9dwZn/+52Fmyqx32doBFy/EPqMq75A5e+SF8KmOc5638Dg8q8X9/U7F0LCYf92Z6q/SA3x1pxE/vvlak7t25pnzh9AUKCbv9rVHdFNGjLz6hEc1bU5d32ygvs/X1X5D7JFpN5y83/iJUAP7/7URcV5n5eW1Mi77ns5zn7axcUB6621WeXcu3AEO9KHfYq4olurCKZdEcfzEwayNyOXcS/9wW0zl7Ino4LrRwMC4MA+WDazegOVv8S/DKGRMOgityORytizAf43BBa84XYkIgBMmZvEfZ+v4uTerXl2/EAl2D4WERbMaxcN4ZIjOvLmnESunLKAjJx8t8MSkVogqCIXGWNuqUSfR1Twug+B23CKkj3jvU8oTuXvOdbabd5jMUBD73ZaRds+bIwZWGRP6+7AaJy9rgvjbmGt3V3CvS/DGZ1eVNk+RWoiYwxn9G/L6B4tee6n9bz5eyLfrtzBrSd044LhHcr/xSvhdfj5v852Um36+Sfo+uyUR2HgBc5MAqk9mnWG6KHww33O9l6R7dyOSOqx9+Zt4j+freSEXq14bsJAgpVgV4ugwADuOaM3nVo04t5ZKzn35bm8MXkIbaNKK9UjIgLGVqCqrTGmsnPjrLU2sAL9fgCMAZ4GNgKTgaHAsdbaOd5rZgNHW2tNkXYRwGKcva6fBPJx9uk2wABr7V7vdfd6+/8SSAKa4OzxHQe8aK29rrJ9VoS3wnhiYmIiHTt2rGgzEZ/ZsCuD+z5fyW/r99CjdQT3n9WHYbFNS29wIBWe6QudjoHz3/VXmPWTpwACyv3vUWqqlER4cQR0PhbGT1PROnHFtPjN/N8nyzm+Z0tenDSYkCAl2P7wy7rdXD91EWEhgbx+0RD6t49yOyQRcVFSUhKxsbEAsd4lwwdVNMk+urI3tdb+UoF+w4AHgAtwEuBlwP9Za38ocs1siiXZ3uPROMn5iTjT3n8GbrLW/lnkmlE4ifIQoCWQizMt/HXgLVvsxVekz4pQki01gbWWb1bs4IEvVjmVyAe241+n9KBl47CSG/z0IPz6GFzzB7Tq7d9g64usFHjlKDj5Eeh5utvRSFXNeQ6+/zec+44K14nfzZi/mTs/Ws7oHi156YJBhAbpQzt/WrcznUvfns+ejByeOm8Ap/YtcYdXEakHDjvJlspRki01SVZuPi/+vJFXf/2TkKAAbjq+K5NHdvz71MKsFHimH3Q9Hs5925VY67xfH4ef/gvXzIVWvdyORqqqIB9eHw2Ze+HGJSpeJ34zc8EW7vhoGUd1bcErFw4mLFgJthv2ZuRw5bsLWbhpH7ef1J1rj+mM0awWkXqnrCRb84tE6riGIUHcdlJ3vr35KIZ0bMJ/v1zNac/9xtyNxVZANGwKcVfCyk8hdbMrsdZpedkQ/yp0OV4Jdm0XGARjXoYLPlSCLX7z0cJk7vhoGUd2aa4E22XNGoUy9fI4zhrQlse/XcttM5eRk1/gdlgiUoMoyRapJ2Kbh/PWxUN59cLBZOUWMOG1efzj/cXsSMv+66IR18PlP0BUjHuB1lXLZkDmLhh5g9uRiC+06gUte0JBHuxLcjsaqeM+WZzMbR8u5YjOzXntoiFKsGuAsOBAnjl/ADcf342PFiVz4esJpGTmuh2WiNQQSrJF6hFjDCf2bs0PtxzNjcd15duVOxj95Gxe/mUjufkeZzQ7eghYC7natc5nPB6Y+z9o3Q9ij3I7GvGlmRfDu2c7MxVEqsFnS7Zy6wdLGdGpmRLsGsYYw43Hd+W5CQNZkpzK2BfnsHF3htthiUgNoCRbpB4KCw7k5hO68cPNRzOyczMe+XoNpzz7K7+v3+Mk2O+OgS9ucjvMuiM7FSKj4YgbVY26rhl6OaRsdIoGivjY50u3cfOMJQyLbcrrk4fQIEQJdk10Zv+2TL9yOJk5+Yx9YQ5zNuxxOyQRcZmSbJF6LKZZQ16fPJQ3Lx5CvsdywRvxXDN1EelNesLymbB3o9sh1g0Nm8KFn0CfcW5HIr7W+VjoPxHmPAs7VrgdjdQhXy7bzk0zljCkQ1PevHgoDUOC3A5JyjAopgmfXHsErSPDmPxmAu8nqLaJSH2mJFtEGN2jFd/edBS3ndiNn9fu4pSEAeSbYAp+edzt0Gq/Xavhz9nODAGNYtdNJz0IYVEw6x/OPugih+mbFdu5YfpiBraP4q1LlGDXFu2bNuSja0ZyRJfm/Ovj5Tz45SoKPNrFR6Q+UpItIoAzhfz60V354Zaj6dOtK+/kjoZlM5i7YL7bodVusx+BGRdBbqbbkUh1adgUTnkUti2CVZ+5HY3Uct+u3MH10xYzoH0Ub186jPBQJdi1SURYMG9MHsLkER147bdErnp3IZk5+W6HJSJ+piRbRA4R3aQhL184mN7n3E0BgWz69L9cMWUBW1JUCK3SUhJh9SwYcgmENnI7GqlOfcbBpI+g91i3I5Fa7IdVO7l+2iL6Rkfy9iVDaaQEu1YKCgzgvrP6cN+ZvflpzU7OfXku29MOuB2WiPiRkmwRKdHwAX0IOOo22vUYxpwNezj+qV945od1ZOdpOmyFzXsJTCDEXe12JFLdjIGuxzvPO1Y4ywNEKuGnNTu5ZupCerWN5J1LhxERpj3Ya7vJIzvy5sVD2ZySxVn/m8Py5DS3QxIRP1GSLSKlChr9T0ZdcBc/3no0x/dqxTM/rOeEp3/hh1U73Q6t5stKgcXvQr/zoHEbt6PxiR1p2WzcnUF+gcftUGquP2fDy0fAio/cjkRqkZ/X7uLqdxfRs01jplw6jMZKsOuMY7q35KNrRhIcGMC5r/zBNyu2ux2SiPiBsfq03eeMMR2BxMTERDp27OhyNCKHKXMv/P4UDL+GObvDuGfWSjbsymB0j5b85/RedGwe7naENdPvT8MP98I1c6FVL7ejqTRrLZv2ZpGQmEJCUgoJiSls9i4ZCAkKoFurRvRo3ZgerSOc5zYRNG8U6nLUNYCnAN44AfZtguvnO+u1Rcrwy7rdXDFlAd1aNWLqZcOJbKgEuy7anZ7Dle8uYPHmVO44uTvXHN0Zo2KYIrVaUlISsbGxALHW2qSi55RkVwMl2VKnpG6B5wbC4Mlw2pPkFXh4e04Sz/ywjrwCy1VHd+LaY7po/9bicjNh48/Q83S3I6kQj8eyble6k1R7H7vScwBo0jCYYbFNGdqxKVENQ1i7Yz9rdqSzZkc6u73XADRvFErPNhF0bxVBjzZOAt6lZSPCguvZ98aOFfDq0dD3PBj7ktvRSA32+/o9XPbOfDq3aMS0K+KIahjidkhSjbLzCrj9w2V8vnQb5w6O5sGxfQkJ0qRSkdpKSbafKcmWOufzG2HJNLhxKTRuC8DO/dk89NVqPluyjXZRDfj36b04qXcrfTIP4PFAQM3+xSmvwMPKbfuZn5hCfGIK85NSSDuQB0DrxmHEdXKS6rjYpnRu0YiAgJL/Xvdk5LDWm3Cv2e4k3+t2ppOT70wpDwwwxDYPp0frCHq2aexNwCNoF9Wgbn+v/Hg//PYkXPips5e2SDF/bNjDJW/PJ7Z5OO9fMZwm4Uqw6wNrLc/8sJ5nf1xPXGxTXr5gsP7uRWopJdl+piRb6px9m+D5QTD0cmeroiLi/9zLPbNWsmZHOqO6Nue+M3vTqUU9rqTt8cDro51RzBHXuh3NQdl5BSzZksp87/TvhZv2kZXrFLGLbR7OsI5NGRrrJNXRTQ4vAS7wWJL2ZrJmezprDo5672dLyl/VdSNCg+jRJoLu3unmPdtE0K1VRN0p9pSXDS+NhPDmcNl3bkcjNczcjXu55O0EOjQNZ9oVcTTTUot657MlW7l95jLaRoXx5sVD6/fPTZFaSkm2nynJljrps+tg+YfOaHZE60NO5Rd4mDJ3E09/v47s/AIuH9WJf4zuQsOQerj9zNqv4f3xcPbr0O9c18JIz85j4aZ9zPeup166JY3cAg/GQPdWEQyLbeo8OjalZeMwv8W0bmfhqLc3Ad+eTnqRPWSjmzQ4mHT3aN2Y7q0j6NisIUGBNXtmQIl2rnKK3jVo4nYkUoPE/7mXi9+aT3STBrx/5XDVMqjHFm5K4copC8n3WF66YBAjOzd3OyQRqQQl2X6mJFvqpJQ/4X/D4MznYMDEEi/ZlZ7No1+v5aNFybSJDOOu03pyWt82dXtacHFvnQqpm+GGxRDov1HZlMzcgwl1QmIKK7el4bHOdO2+7SIPJtRDOjapUes+rbVsS8s+ONV89fb9rN2Rzp97MinwOD+fQoMC6NaqcNTbmXbeo3VE7Rn9y9wLB/ZB8y5uRyIum5+UwuQ3E2gb1YD3rxhOi4ha8j0s1WZLShaXvj2fxD2ZPDi2D+cPjXE7JBGpICXZfqYkW+qs9B1/G8UuyYKkFP7z2UpWbd/PEV2acd+ZvenSMsIPAboseaEzVfykh2DEddV6q+1pBw4pUrZ+VwbgJKQDY6IY1rEpw2KbMTAmivDQ2jejIDuvgA27MrzrvQsT8HT2ZPy90FphhfPuNbHQmrXw0hEQEAhX/AyBte/vQnxj4aYULnojgVaRYUy/cjgtI/wzg0Rqvv3ZeVw3dRG/rd/DVUd14s6Te5RaB0NEag4l2X6mJFvqNI8Hdq8pd1uqAo9lWvwmHv92LVm5BVx6ZCw3HNeVRrUw4auwmRfDhp/glpUQ6rsPFay1JO3NOlikLCFp78H1zY1CgxjcoQnDvOup+0ZHEhpUg5JMHysstLbaO/K9dkc6a3emk1uk0Fqn5uEHq5v3aO1UOm8bGebejIqVn8LMyXDCA3DEDe7EIK5atHkfF72RQIuIUKZfOZxWflqiIbVHfoGH+z5fxbvzNnFir1Y8M35A5ZZcpfwJ3/yfs6PFwAuqL1AROUhJtp8pyZY67ft7IOFVuGm5U9SpHHszcnjsm7XMWLCFlhGh3HVaT87s37buTSH3eODjy6FJRzjuP4fZ1V/bacV7R6oLt8pqGh7C0I5NGBbbjLjYpvRoHVE71yv7UH6Bh6S9WazZsd+bgDuj38n7ihRaCws6ZE/vwpFvv3zoYy1Mn+hs6XbtXGgaW/33lBpjyZZULnw9nmaNQph+5QhaRyrBltK9PSeR+79YRa+2jXn9oqHlf79Y6+z+8fUdkJsBl3wNHUb6J1iRek5Jtp8pyZY6bfc6eGEYHHEjnHBfhZst2ZLKfz5bwbLkNOJim3L/WX3o3roOTiG3Fir5AULhdloJiXtJSExhftK+g9tptYkMO1ikrHA7rTr3AUU1KSy0trpIkbW1Ow4ttNa+qVNorWgC3rFZOIG+nqqZthVeiIPowc62Xvo7rBeWJacy6fV4mjQMYcZVw2kT2cDtkKQW+HnNLq6ftohGYUG8MXkofdpFlnxhVgp8cTOs+hQ6HAljX4ao9uApgAOpEN7Mn2GL1DtKsv1MSbbUeR9eCmu/8Y5mV/yHeIHHMmP+Fh77dg3p2flcNKIDN5/Qjca1fdumrBRI+h16nOasvS1H4XZaheupF27ax4E8ZzutTs3DGdqx6cHE+nC305JDWWvZmnqg2PZi6fy5OwNvnbWDhdYKp5r3bO0UXTvsQmsJr8FXt8PlPzrJttRpK7amMfG1eUQ2DGb6lSNoF6UEWypuzY79XPb2AlIyc3lm/ABO6l1CPZQ3T4bk+TD6bhh5w18/fz6YDPsS4dJvIVjfdyLVRUm2nynJljpv12p4cQSMuhWO+3elm+/LzOWJ79YyLWEzzcJD+dcpPTh7ULvam0z+8jj8/F+4dh607Pm304XbaRUm1cuSD91OKy7WKVI2NLaJiiG5pLDQmrO92F/Jd9FCay0iQg9WN+/eKoIebZxCaxVeA+/xwK6V0LpvNb0KqSlWbktj4mvxNAoNYsZVw4lu0tDtkKQW2pWezZVTFrI0OZV/ntyDK4/qhCnIhdxMaNgUtsx3iim2HXhow8KtJPuNd0a3a+vPVpEaTkm2nynJlnrhg8mw4Ue4aZnzw74Klien8e/PVrBkSypDOjThvrN607ttKdPiaqq8bHimD7TpDxd8BDjr0OcneZPqpL2s2rYfj4WgAEOfdpHepLopQzo0JbJhLR/Fr+N2p+ccUuF8zY79rNuZUWqhtZ5tIujeupxCax4PJM6GzqP990LEb1Zv38/E1+bRMCSI6VcOp31TJdhSddl5Bdw6cylfLtvOP/oUcHP6YwQ0agEXfFx28jz7UZj9EJz8CAy/xn8Bi9QjSrL9TEm21Au7VkPyAug//rD2g/Z4LB8uTOaRb9aQmpXLBcM7cOsJ3WtP8rnwbfj8RuYc8SZfZnQjITGFDcW30/IWKRsYE1W5arFSIzmF1jK9o95/JeDFC6319K7x7t66WKG1he/A5zfApA+h6wkuvhLxtbU70pnw2jxCgwKYceUIYpopwZbD5ynw8NN7D3Pkn0+TG9iQgLP+R6P+Z5bTyAMfXOiMal/0KcQe5ZdYReoTJdl+piRbpPLSsvJ46vu1vDtvE1ENQ7jz5O6cO7h9jdsrtHA7rYTEvST8uYcb1lxAekEwp+c+SKPQYIZ0/Gs7rT7t6vZ2WnKo/dl5rNuRzuod6az1FlpbsyOdjCKF1mKaNqR3q1D+u+NaGpoctk+aTYc2LX1faE38bt3OdCa8Oo/gwACmXzmcjs3D3Q5J6oKM3fDZdbD+W3a0PJKzt15AWJM2vHHxUGLL+x7L3g+vH+csUTnnTf/EK1KPKMn2MyXZUm94CpzKpk06wqhbfNLlym1p3PPZShZs2seA9lHcf1Zv+kVH+aTvqvB4LGt3ph9cTx2fmHJwne6ZDZfznOdhZvd5mOYjJtGzTWMlS3IIay3J+w4cnHK+2rvmu+neRcwMuY838k/hcTP5YKG17q2dQms92zSmSXiI2+FLBW3Ylc74V+cRYAwzrhpRfvIjUlG/PQWzH4ETH4BhVzJ/0z6uenchHmt5+YLBDO9UTvHR9B3QsLmzdltEfEpJtp8pyZZ65f0JsOkPp9J4WGOfdGmt5eNFW3n46zXszcxhwrAYbj+xu1+SjrwCDyu2ph1MqucnpbA/2xmJbHtwO61mDIttSuemwZjVn0Ovsw5ryrzUP9l5BWR+fCNNV0/lrR6v8lNGDKu372dvZu7Ba6KbNKB/dBT9oiPpFx1F3+hI/+zrLZWycXcG41+dB8D0K4fTuUUjlyOSWi/vAGyJh07HQEG+Uym8edeDpzfvzeKStxPYnJLFg2P7ct6Q9uX3uXUhrPwETnhAhdBEfERJtp8pyZZ6ZdtiePUYGP1vOOo2n3a9PzuPZ75fzztzk4gIC+L2k7ozfmiMT0eLs/MKWLw59WCRskWbUg/ZTqtwKy1nO60i6yursB+2yCGy0+CF4c4v0mNfAv4qtLZyWxrLktNYmpx6cK23MdC5RSP6RUfS35t092rTmLBgLUlwy5/eBNtjYfqVcXRpGeF2SFLbbV8GH10O+5KcwqIRJWzdBaQdyOP6aYv4bf0erj66M3ec1L3s5VWFu2CoEJqIzyjJ9jMl2VLvTD0PkhOc0exQ3/+SuXZHOv/5bAXxiSn0bRfJ/Wf1ZmBMkyr1tf9v22mlkldgMQZ6tG78V+XvjuVsp/XRFdCwGZzySBVflQiwd6Oz3KKM/dX3ZuSwbGsay7aksSw5laXJaQeXLAQFGLq3jqBfdBT9vSPe3Vo1IigwwE8voP5K2pPJ+a/OJb/AMv3K4XRtpQRbDoPHA3P/Bz/e7/xsGfMidDmuzCZ5BR7unbWSqfGbObl3a546v3/pxTVVCE1c5vFYMnLzScvKY392HvsP5JN2oPDPeezPzneeD/x1PjjIMPXy4W6HXiol2X6mJFvqneQFTnGV4++DI2+qlltYa5m1dBsPfbWanftzOG9INHee3INmjULLbOdsp+WspZ6flHLIdlp9oyMPFikb3KEpkQ0qOOU7JRGeHwQjb4AT7vPBq5N6b/syCAmHZp3LvdRay4792Sz1Jt3Lkp3nwmUNYcEB9G4beciId2yz8BpXRLA227Q3k/GvziMn38P7Vwyne2sl2HIY9m+HT66CxF+gx+lwxnMQXs5aay9rLW/OSeK/X66iT9tIrju2C41Cg2gYGkh4SBANQwIJD3WeQwsyMK8fD1l74crZEBVTva9L6hRrLQfyCpzE+ED+weQ47cChSXJakSR5f/Zf59Nz8ikv7YwIC6JxWDCNGwQT2SCIlhFhPDdhYNmNXKQk28+UZEu9NG08tOhe7UlnRk4+z/+4njd+T6RhSCC3ndSdSXEdDk4h35Z64GCBsvlJh26nNSjmr8rfAw5nO62vbocFbzkj943b+OqlSX2Vm+Xstd6yF0z+vErLEAqr3i9LTj2YfK/YlkZ2nrOfd0RYEH3bRf414t0+quy9vKVUW1KyOP+VuRzIK2DaFcPp2cY3tSikHtuXBG+cBMf+Hwy6qEr/B/y4eic3vL+YzNyCUq8JDDD0CtnJ+/wfWwPacGeTpwkLDXGS8dAgwr0JeXhI4MGvG4YEER5a+Oz8uWjyHhoUoP9HapGc/IISR5DTio0g7y9yPq1IAp3vKTtvbBgS6E2Sg4hsEHwwYW4c5v364LGgg38uvK5RWFCtKx6rJNvPlGRLveTxQID/pqhu2JXOPbNWMmfDXnq1aUyP1hEkJKUcXL8aERrk3U6rGcNim9C3XRQhQT6ILysFnu4Nvcc60/lEfMG73zpnPu/8ku0D+QUeNuzOYNkWZ233suQ01uzYT16B83O/eaMQ+h0srOYk4M3LmRlS321JyWL8q/PIzM1n6uVx9G4b6XZIUlvlZMDvT8GoW51ZLHnZEFzGEqUKSMvKY2vqATJz88nMyScrt+Cv59x8snIKyMjJJ2bvb+TnZjM3ZCSZuQVkFTlXeG1F04MAA+HeBLyk0XMngQ90RteLJuxFEnknsf+rfViwEvfS5Bd4SM8uOkJ86GhxadOwC8/n5HvK7D8kMODgKHLjIklyZIOgIgmzNzFuUHTUOZiIsCCC69lSJSXZfqYkW+qtgjxYPhN6jYGQhuVefristXy9YgcPfrma7LyCgwXKhnZsWn3baRUWj7lmLrTq5fv+pX7yeOCdM2DncrhuPkS0qpbbZOcVsGZH+iEj3ht2Zxz8hbpdVIODCXf/6Ej6REfSOEyV8wG2ph7g/Ffmsv9AHtOuGE6fdkqwpYqSF/xV3Gz8NOhxqjtxWAu7VkGr3sUOW7LzPAcT88KkPTO3gKzC59x8MnOc54ycv64rnthnFjlXziDoQcabuDcMCTw49b14Yl6YsB9M0Ism9qFF2nqvaxAcWCMS98J1yX9Ns84vccT4YIJc7HxZMxXAma1Q0qhx8VHlxg2KJsx/JdQqpFk5SrL9TEm21Fub4+HNE+Gkh2HEtX67beH/Y375Afr9PbB3A4yfWv33kvplzwZ4aaTzC/e5b/vtthk5+azYmsby5L9GvDenZB0836lFeJGtxCLp3Tay3v0iti31AOe/OpfUrDymXT6cvtFKsKUKCvKd0evZj0DjdnD2K9BhpHvxzH/DWf7kh0Jo1lpy8j1/S8ALE/WDz95EPqPY10UT+MwiCX1BBTN3Y6BhcOmj58WT9L8S+r9fEx4aiMdD6SPIJY0oZ+eRllX+umRjnJl4pSfIJRwr8nXDkJrxYUJ9oSTbz5RkS7329umwZx3cuBSCG7gdTfXQ9l1SXX59HOY8D9fOhch2roWxLzPXW9HcqWa+LDmVXelORfPAAEO3VhEHq5n3i46ke+uIOjtNcHvaAca/Oo+UjFzeuzyO/u2j3A5JaqPcTHj3bNgyD/qeB6c9AWEuf1iTvd8pWlpLC6EVJu5FR8+dKe9/T9ozD15TcmLvJO/OnyuauJcmPCSwwqPIxc83Cq1965LrMyXZfqYkW+q1xN/gndPhlMcg7iq3o/EdjwdWfeJUfg3SulWpJvm5cCCl1L1x3bQjLftgNfPCEe+0A3mAU1iwV9vGRUa8o+jUvPZXNN+5P5vxr85jT3oOUy4bVuWtA0UA+PJWaB8H/c5zO5K/7FkPr412thK87Lu6++F4BVlryS3wHDL9/dDp8M4oe6AxB9clF02S6+O65PpMSbafKcmWes1aeOtU2JcINyw57EIuNcbar+H98XDOW9DnbLejkbouNwtmPwRH31kte8/7grWWzSlZzkj3FifpXrEtjSzvmsFGoUH0aVeYeDvJd3STBrVmKuMub4K9c382Uy6LY3AHJdhSSQdS4ctbnDolvc50O5rSrf3G+fnW7zwY+4pmaolUUFlJdhX3rxERKYUxcPQd8O4YWPUp9B/vdkS+Mec5iGwPPWvwL0pSd2xbDHNfhJ0rYeIHEFjzio8ZY+jQLJwOzcI5s39bAAo8lg27Mg7Zv/utOUnkFjgVbZuGhxxSWK1fdBQtImrezJBd6dlMeG0eO/ZnM+XSYUqwpfKSfoePr4KMHRA9zO1oytb9ZGf7sBUfQXYqNND3u8jh0kh2NdBIttR71sKfP0PsMX7d1qvaJC9w1q35uaCb1HOLpsCsf8CAC+Cs/9Xa0aWc/ALW7kg/ZMR7/a70g5WG20SGFUm8o+gbHUlkA/c+VNiTkcOEV+exNfUAb18yjGGxTV2LRWqh/FxnFsrvz0DTTjDuNWg32O2oyufxQP4BZzsxEakQjWSLiH8ZA51HO3/Ozaz9P7T/eA5CI2HQhW5HIvXJoIsgbSv88ghERsOx/3I7oioJDQr0ThePguEdAMjKzWfltv0s3fLXiPe3K3cebBPbPPyQEe/ebSNpEFL9Fc33ZuQw8bV5JO87wFuXDFWCLZX38RXOLK5Bk+GkhyC0kdsRVUxAgPOzOnUzzLoBznyu1hVCE6lJlGSLSPX5+WFYNh2uX1Ajp7tWyL4kWP05HHFjjV0bK3XYMf+EtGQn0W7T3739dH2sYUgQQzs6e9oXSsvKY9lWb2G1LanE/5nCZ0u2ARBgoFuriENGvLu3jiAkyHczZVIyc5n0ejybU7J48+KhDO/UzGd9Sx1nLeRmOD8jRlwPfc+Bnme4HVXV5OfC1kUw4wK49Nt6XwhNpKo0XbwaaLq4iNe672DauXDm/2rvKLDHA+u+gXaDamTFZ6kHCvJg3osQd3W9q2y/a382S5PTWJ7811Zi+7KciuYhQQH0bNP44Nru/tGRdGrRqErb3+zLzGXi6/H8uTuDNy8eyhFdmvv6pUhdlbnHWdaRkw4XzaobS6TWfQvTzlchNJFyqLq4nynJFvGyFl49ximkcv1CCKxlk2e0H7bUNDtXOkl32wFuR+IKay3J+w4c3EJs6ZZUVmxNI9Nb0Tw8JJDe7SKLJN5RtG9adkXz1CxnBHv9rgxev2gIR3Vr4a+XI7Xd+h/gs2vhwD44/j7ng7C6kGQD/PIY/PygapGIlEFrskXEHcY4WxBNnwDLP4ABE92OqHJ+fwq2LYFz3qy9092l7vB4vNWKd8Ll3zv72tYzxhjaN21I+6YNOb3fXxXN/9ydcciI9ztzN5GbnwhAk4bB9C1Szbx/dCQtGztbC6Zl5XHhGwms35nBqxcNVoItFZN3AH64F+JfhhY94YKPoXUft6PyrVG3wfal8N3d0OkYaNXL7YhEahWNZFcDjWSLFGEtvDLK2ff3uoTaM5qdlw3P9IE2A+CCD92ORsSxey28cSKEt4DLvoOGKsxVktx8D+t2pjsj3lvSWJqcyvpdGRR4S5q3bhxG3+hIkvcdYOOuDF65cDDH9mjpctRSayS8Bl/d5oxcH39v3V23nJMOKz+BgRdqVpdICTSSLSLuMQaOvQsSf4P8bAisJZVWl02HzN1wxA1uRyLylxbdYcJ0mHIWvD8eLvqs7v6CfxhCggLo0y6SPu0imRTnHDuQW8DKbWkHR7yXJaeRkpXLSxcMUoIt5fN4YPtiZzuuIZdCqz7QYYTbUVWv0AhnlwOAxF+h3RAIaehuTCK1hEayq4FGskVqOY8HXhjm/DJx5S/6BF9qnpWfwsyLnQrG503R92gVWWvLXK8tAsD+7fDpNZD0G1w7D5p3dTsi/0pJhOcHQ59xcPar+v9GxEsj2SLivoI8mP+6s460+yluR1O2dd/A3vUw7g39MiE1U+8xkP4IhEXqe/QwKMGWcq3+3Nk3Ou8AnPo4NOvidkT+1zQWjvkX/Pxfp+jiiOvcjkikxlOSLSL+YQJh4dvOn7ueVLMrsGbsgJa9oNdZbkciUrrhV//15x0r6l7hJRE35WTAt/+CRVOc2hzjXq9/I9hFjboVti+B7/7tTJXvdLTbEYnUaDX4t1wRqVMCAuCo22H3Glj9mdvRlG3IpXD1HFUUl9ph+Yfw8pGw4iO3IxGpOw7sg1Wz4Mhb4LLv63eCDc7P8LEvO+/DzIshdbPbEYnUaEqyRcR/eo+F5t3gl8eddc810dpvnIqqNXmkXaSoHqdDzHD45GpImuN2NCK1l6cA4l9xRrGj2sONS+D4eyAoxO3IaobQCBg/DRo2cwqDikip9FukiPhPQKAzmr1rJaz5wu1o/i7lT2dP7znPuh2JSMUFhzm/+Dbp6Hz/7lrtdkQitU/qZnj7dPj6Dlg+0znWoIm7MdVEzTrDdfFOlXWPx9mmU0T+Rkm2iPhX77OhaWdY/63bkfzdvJecteNDL3c7EpHKadgUJn0IQWHw3jlONWQRqZhlH8BLR8CO5TD2FRh8sdsR1WwBgZCbBe+fD/NedDsakRpJhc9ExL8Cg+DSbyC8hduRHCorBRa/B/3Oh4jWbkcjUnlNOsCkmfDptZCX5XY0IjVfQT58erUzct1+OJz9ijMjRMoX3ACCQlUITaQUGskWEf9r1NLZdmjH8poz1Wz+G05iMvJ6tyMRqbo2/eGq35wpnblZkJ/rdkQiNVdgkJMsHnsXXPylEuzKMAbGvKRCaCKlcDXJNsaEGmMeNcZsM8YcMMbMM8YcV8G27YwxHxhjUo0x+40xnxpjYotd094Yc68xJsEYs88Ys8cY83NJ9/BeZ0t47PDV6xWRItZ/71REXv+925FAfg4kvAJdToCWPd2ORuTwBAQ4yfWUM+HzG2vOB1kiNUFBHvx4P6z8xPn6jOfg6DuchFsqp7AQmqcApk9yPtgTEcD9key3gZuB94AbAQ/wtTFmRFmNjDGNgJ+BUcCDwD3AIGC2MaZolYqzgDuADcDdwANAY+AHY8yFpXR/FXBhkcd1VXlhIlKOTsdAVAz88oj7SUBgCJzzFoy+2904RHwlKMT50GjpNPj5IbejEakZ9myAN06E356E5AXOMWPcjam2a9bZ2UN8x3KtzxYpwrWP7Ywxw4DxwM3W2me8x6YAK4BHgaPKaH4t0AUYbK1d7G37tbftzcB/vNf9DMRYa/cUue/LwBKchPvdEvr+wFqbWtXXJSIVFBgMo251Rto2/ghdjncvFmMgdpR79xepDkffAWlb4NfHILKdijlJ/WUtLHoHvvmX86HqeVOg11luR1V3dDsRLvgQYrUuW6SQmyPZ5wB5wOuFB6y12cAbwJHGmDbltJ1XmGB7264BfgTOK3JsZdEE23ssB/gK6GCMaVBC38YY09gYfbQpUu36T4TG0TD7UfdGs9d+De+cAelaGSJ1jDFw+tPOiPYXt8C6GljRX8Qfvv0/5wPd6KFw7Vwl2NWhy/HOh+fbl0LSHLejEXGdm0n2QGCNtTaj2PEEwAADSmpkjAkA+gELSjidAHQzxjQs596tgQwgu4Rzm4E0IM0Y86Yxpmk5fYlIVQWFwKibITkB/pztTgxznoOURGjY3J37i1SnwGA4921o3QdWz3I7GhH/KshznvueAyf+Fy78FBq3dTWkOs1a+PwmmHEB7NvkdjQirnKzykMbYGsJxws39yztf8GmQGiR64q3Nd6+N5bU2BjTBTgbmG7tIUNn+4DngXlALjAaZ332IGNMnHcEvKT+ooCoYoejS4ldRIobeCHkpEPbAf6/d/IC2PwHnPSwit5I3RXaCC6aBaGNna+t1TpUqdvysuHH+2D7Mpg8C9oNdh5SvYxx1me/eizMmASXfgch5Y17idRNbo5kNwBKSlyzi5wvrR1Vaesd4Z4JZAL/V/SctfZZa+0N1tpp1toPrbXXAv8A+gMXlfoq4CYgsdjjtzKuF5GigkLhyJuhQZPyr/W1P56DsEgYVFodRJE6okGUU3U86Xd4/TjI3Ot2RCLVY+dKeG20U4SrVS/w5LsdUf1ysBDaCvj8BvcLm4q4xM0k+wDOiHRxYUXOl9aOyrY1xgQC04GewDhrbUkj4cW9DGQBZW0r9gwQW+yhCkoilZXwmjPFzF9S/oTVn8OQS51tSETqg4Ag55ff98dDXmk/ZkVqIY8H5r3kjKJm7oKJM+HUx50PcsW/up0Io++C5TNh7gtuRyPiCjeT7O0407qLKzy2rZR2KTij2KW1tZQ8lfw14DTgImvtLxUJ0FrrwZnSXuq6bGttqrU2qegDSK5I/yJShCffSXr9VTBl70aIaAPDrvLP/URqgpjhMO41SJ4PH13u7G8rUhes/Bi++Sd0PhaumeskeuKeUbdBv/HQuKw6xiJ1l5tJ9hKgh3fP66LivM9LS2rkTXyXA0NKOB0HrLfWZhU9aIx5HLgEuMla+0FFAzTGBAPtgd0VbSMiVTRoMoS3hF8e9c/9up4ANy3XLwBS//Q6C05+BNZ84SQlms4ptdlebwme3mOdrbkmTIdGLdyNSZz12We/An3GOV/nFK9zLFK3uZlkfwgEA5cXHjDGhOIkw3Ostdu8x2KMMT1KaDvcGDOwSNvuOMXKZha90BhzO3Ab8JC19vnSgjHGlPQ/8u04U9C174lIdQtpCEfcAIm/wOZ51XuvTX84a1IDAqv3PiI11fCrYcT1sOBN2LXa7WhEKi8309mW639DYdsS5//zXmepqF9N9P098NbJkJtV/rUidYSxLn6CbYz5ABgDPI1TDXwyMBQ41lo7x3vNbOBoa60p0i4CWAyEA08C+cAteLf+stbu9V43FvgYWA/cX0IIn1hrM73XZuGs2V6BMx39WGAc8Ls3ngpXzjDGdAQSExMT6dixY0WbiUhuJjzTF9oMgAs/rp575GXDM30gehhMmFY99xCpDTwe2Lkc2vR3OxKRytm22FnusHej8+HssXc7W0JKzbT+e5h6rjOqPe51fRAidUZSUhKxsbEAsd4lwwe5vWfNRcAD3ucmwDLg1MIEuzTW2nRjzDE4yfm/cUbkf8aZDl60ZGrhbw5dgXdL6CoWp9I4wFTgCOBcIARI8sb2cGUSbBE5DCHhMPIfkPirU5QpuLRNBg7DsumQudsZyROpzwICnATbWpj9MHQcBbGq2yk1mKcA5jwLPz/oLC+aPAtij3I7KilP1xNg9N3w0wPQdiCMvN7tiESqnasj2XWVRrJFDoPH4/zyX119vzDMSeavnK1P00XA2af+9eNh/3a47Fto2dPtiERKlr7T+T+80zFw+tPQsNS6tFLTWAsfXAhrvoQLP3H+DkVqubJGst1cky0i8ncBAc4P440/w85Vvu173Tewd70zWq4EW8QRGgGTZjozR947B/aXtrmHiEtWzYLs/RDRCq7+Hc59Wwl2bWMMjHkJmneDnx9SwUWp85Rki0jNk5sJMy92ppb50h/PQ2QM9Brj235FaruoGCfRzk511k5m73c7IhHn+/Djq5wR0PiXnWNR7fUhaW1V+IHepJn6O5Q6T0m2iNQ8oY1gxHWw9ivYXuJuflVz8sPOFMNAt8tRiNRAbfo5WyDtXgPf/MvtaKS+2zwPXj4Cln8Ax/wLjrzF7YjEF6JiICwS0rbC7Ec1oi11lpJsEamZhl0JoZHwy2O+67PtAOh6vO/6E6lruhwH570Lx/3H7UikvrIWfnoQ3joFTABc+i0c8099OFrXrPwEZj8Ec//ndiQi1UJJtojUTA2iYPg1sOYL2LHi8PpK+RNeOw52LPdJaCJ1Wo9TnbWvmXth4dtuRyP1jTGQsRP6T3DWX7cf5nZEUh1GXAc9z4Tv/+PUYBGpY5Rki0jNNfxqCImA+JcOr5+5LzrTzsNb+CYukfog4RX4/EZY8KbbkUhdZy0sehdWfOx8ffrTMOZFZw2v1E0HC6F1hw8vhX2b3I5IxKeUZItIzdWgCVzwIZzyeNX7yEqBxe9Bv/MhorXvYhOp64664//bu+8wqcq7/+Pv79KbgKioKEIUW6IRgx0htiR2jWiM2BuxRY1PTDXG5PH5xSQmxm7sUSyIihrFGBUVCxYCFuwIKs2AiAjS9/79cWbjutkFFmb3zOy+X9e115m555yzn9Vhr/3O3aDPt+DBc+Cth/NOo6bq89kw7Gi4/3R47e6sraJFvpnUONp0hMOHQloGdw6GJQvyTiQVjUW2pNLWc0do3R4WzVu161+8DpYugJ1PL24uqalr0RIG3QjrfR2GHwdTxuadSE3Ne0/AVTvDWyNhr99k6wGoeem2MRxyPXz1YGjZNu80UtFYZEsqfe/8Ey7eHGa+Vb/rliyE56/JeuPW2aJhsklNWZuOcMSwbKrFbYe5h7aK57kr4G8HZkPCT3oMdjkTKvyztFnqsxfsek42hPzTqXmnkYrC32aSSt/620KqhKf+WL/rZr8HrdrDzj9smFxSc9BxHTjyHuh3HHR0yoVWU9WWTb36Z7tInPxkNlpCevMhuHQbF0JTk2CRLan0degG250Arw2HWe+u/HXdt4Qfjsv+mJO06tbaBHb/ZdbTOGUsLP4870QqNynB83/Neq+XLc0K633+kE0HkgB6D4A1N86mp3wyOe800mqxyJZUHnY+A1q0gdEXr9z5H02AT6dk80ojGjab1Fx8NgNu2hfuPhEql+WdRuXis49g6KEw8sfQojUsXsU1NtS0/WchtEq440g/zFNZs8iWVB46rgP9jodX7syGga/Ig+fAzft/MTRR0urrtG62QNVbD8LIc/33peVLCV65K1vcbPLobKeIwXdBuy55J1OpqloI7aPX4P4z/B2jstUy7wCStNJ2+SHMm7Hi8z58ET54Dr7zO3uxpWLb4WT49EN49lLovCH0PyvvRCpVbz0E95wIPb4BB17hApRaOX32yqanjLkyG5HWZcO8E0n1ZpEtqXx0WhcG3bDi8567DNp2hr5HNXwmqTna8wKYOxUePR/W6AFbH5p3IpWKpYtg0mjosydsuvcX2zO597XqY9dzYNujs1FsUhlyuLik8vPmQ/D0n2t/bfZ78MYD0O+EbH6XpOKrqICDrsqKqHZd806jUjH5abi6P9x2aPa7uKICthpkga36i8gK7IVz4d5TXAhNZcciW1L5mfgYPH4hzPnwv1977kqoaAk7DGn8XFJz0rINHHFH1mOZUrYompqn+R/DiNOyRfGWLoIj7oI1v5J3KjUFn8/K1oBwITSVGYtsSeWn/9nZsbbe7B1PgQMuy4aWS2oc//wVXLs7fDo17yRqbJOfgcv7wSt3ZL+bTx2TffAiFcOaX4FDbigshHa6C6GpbFhkSyo/nTeAvkfCuFv++4/6bhvD1w/PJ5fUXG19WDasc+ihsPDTvNOoMSxZmB3X3gw22A6GjIY9f+2+1yq+PnvCHufBa3fDs5flnUZaKRbZkspT/7OzvTSf+Uv2fMkCuGFveOfRfHNJzdG6W8H3boFZb8GdR8LSxXknUkNZsjCbrnN5P1gwBzqsBYOHQfct806mpqz/j2DLA7PFFj8Yk3caaYUssiWVp64bwTZHwMTHYdkSePkO+ODZbJ6opMa38W7ZNk2TnoL7TnNYZ1M0cRRctRM89XvouVP2QafUGCLgwCth4E9g/b55p5FWyC28JJWvvX4DrTpAtIDnLof1toFe/fNOJTVfXz8829f2jfth0dxsKz2Vv3kz4R8/h1eHZXNkjxqRfagiNaY2HeGbP80ez3wrmzrWukO+maQ62JMtqXy16wotW8OL18LH78LOZ2SfdkvKz67nwPH/yArsZUvyTqNimPU2vD4CBpwLpzxnga18zf8YrtsT7j/DETMqWRbZksrb57Nh5LnZ4y0PyjWKJLIPulq1y3q0r+6f7Wuv8vPvN+CRX2ZFTK9d4KzXYPdfQKu2eSdTc9ehW7YuiwuhqYRZZEsqb+3XhAMuh6PvgxbOgJFKRruu0Ko9DD8epryUdxqtrMWfw6MXZB+QjBsKn0zO2jt1zzWW9CX9z/5iIbSJo/JOI/0Xi2xJ5W/bo+Ar38w7haTqWneAI4Zlxdlth8HHE/NOpBV551G4ckd4+k+w9ffg9Jdgzd55p5L+W9VCaGttBsOP++LDIKlEWGRLkqSG0XFtOPKebMjxrYfA/Fl5J1Jd3vg7DD0EWrSGY/4OB12ZDcuVSlWbjnD4UGjRBj56Pe800pdYZEuSpIbTbeOsR/uz6fDq8LzTqLrKSpg6Nnu86bdh7z/AKc9A713zzSWtrG4bw5njYfN98k4ifYkTGCVJUsPacDs49bls+yeVhhmvwgNnwfSX4YyXoGsv2OHkvFNJ9deqHVQuyxbq67Qe7PLDvBNJ9mRLkqRGUFVgT7gXRv7UrXfysmge/OMXcM3AbB7rQVdCl43yTiWtnqiAuVMLC6E9nncaySJbkiQ1omnj4fmr4Ok/552k+fnwxWxhs+cuh75HwukvwtaHZYtISeWsaiG0tTfPdjRwITTlzCJbkiQ1nj3Oh68NgscugJfvzDtN81BZmR07dYeO68BxD8MBl2ZbIEpNRdVCaKkS7hgMi+fnnUjNmEW2JElqPBUV2RDlXrvCfafBe0/knajpqlwGY66CawZk+1936QknPgYb7ZR3MqlhrPkVOOQG+GgCPH5h3mnUjFlkS5KkxtWyDXzvVlirD9z7A1iyMO9ETc+0cXDtbvDwT7Me7MXzsnaHhqup67MnHHojDPifvJOoGXN1cUmS1PjadYHBd8H8mdCqbd5pmo6Fc2HUhfDCX6HD2jDoRvjqwRbXal6+enB2/GwGfPI+9Nwh3zxqduzJliRJ+ei8AazfF5YtgVH/DxbMyTtR+ftgDDx/DfQ7Hk57Ab72XQtsNV8jToXbDoPZk/JOombGIluSJOVrxqsw+mK480hYuijvNOVnzgfw9CXZ402/la0avu/F2WgBqTnb94/Z8c4jXQhNjcoiW5Ik5avHttliaJNHZz1PVatha/mWLYFnLoUrdoAnf58Ni4VsrrukbCG0QdfDv1+H+06HlPJOpGbCIluSJOVv68Oy7b1eG55t76Xl+/BF+Os34Z/nQe+BcNrz0HWjvFNJpWeTPWGPX8GEe+DZy/JOo2bChc8kSVJp6H82fDoFnrkEun8Ntj4070Sl6c0Hs32AO62XrdK++X7Ou5aWZ5ezsmkpTkdRI7HIliRJpSEC9vkDrLEebPadvNOUlpRg9nvQbWP4ym4w8Cew8+nQplPeyaTSFwHfvQ4qCoN4ly2FFpZBajgOF5ckSaWjogUM+HFWPH7yPkwdm3ei/M2eBEMHwdW7wqdToXV72O1nFthSfVQV2GNvgut2dyE0NSiLbEmSVHpSgnt/ALcOglnv5p0mH8uWwOg/wZU7Zltz7XEedOyedyqpvHXeIBs67kJoakAW2ZIkqfREwIGXZ8ehh8C8mXknalzTX856rh+7IFu46bQXYMdTHOIqra4vLYR2ad5p1ERZZEuSpNLUbWM4Yhh89hHcdljzGt7Zsi0sWwTfvwMOHwqde+SdSGo6djkLvnowPPprmPh43mnUBFlkS5Kk0rVBPxh0A0wfD8OPh8pleSdqGCnBK8Pg5gOyYeJrbwanvwSb7Z13MqnpiYADr4C1t4AHz8kWQpOKyDFHkiSptG2+T7bq+JIFEE2wf+DjifDgj+C9J6BHP5g/K1thvaJF3smkpqt1h2yUCDgNQ0XnO0qSJJW+7U784vGsd2CtPvllKZali+CZv8BTf4SWbWCfP0K/4y2upcayZu/suHg+jLsVtj/ZPedVFE3w42BJktRkvfMoXLE9jL897ySr7+2HYdSFWU/96S/C9idZYEt5ePUuGHlu9qGXVAQW2ZIkqXz0HgC9doX7Ty/PBYvmf5z1mAFscQAc/wgcehN0WjfXWFKztu0x2UJoj10A7z6Wdxo1ARbZkiSpfLRsDd+7BdbaDO48OtvvthykBOOGwuX94IGzYM6H2bDUnjvknUxS9YXQhh8PsyflnUhlziJbkiSVl7adYfBd0HYNGHpoVrCWsplvw037wX2nwlqbwpCnoMuGeaeSVF31hdDuGNy8tgxU0VlkS5Kk8tO5Bwwenv1hvGB23mnq9tbDcNXO8NGrsP9f4LiR0H3LvFNJqs2avbMtA3v0bZo7GajRuLq4JEkqT923hNNeyBYLW7YEUmW2SncpmDcTOq4NPXeEbxwDA38CHdfJO5WkFdlkj+wLYOGn2cgZqZ78iEaSJJWvihZQWQl3HAEjTske52neTLj7JLhiu+xxuy6w78UW2FK5mfISXLK1C6FpleRaZEdEm4i4KCKmRcSCiBgTEXus5LU9ImJYRMyJiLkRMSIietc4Z8OI+HVEvBARn0TErIgYVdf3WJl7SpKkElNRARvtDK/dDY+en0+GykoYexNc/g2YcC9sdxK06ZhPFkmrb50tYI0ehYXQ3ss7jcpM3j3ZNwFnA7cCZwKVwMiI2Gl5F0VER2AUsCtwIXA+sC3wRER0rXbqgcC5wLvAL4HfAmsAj0bEUat4T0mSVGp2OSsrbJ+9FJ6/pnG/98y34Ma94YEzoftWcMozsPsvoFW7xs0hqXi+tBDakS6EpnrJbU52RGwPHA6cnVK6pND2N+A14CJgwHIuPxXYBPhGSmlc4dqRhWvPBn5VOG8U0DOlNKva970aGE9WcN+yCveUJEmlJgL2vgjmToORP4E11oct9m+c7714PsyeCAdeCdsckWWRVP6qFkIbOgjuOw0G3ei/b62UPHuyBwFLgOuqGlJKC4Hrgf4Rsd4Krh1TVQwXrn0TeAw4rFrbhOoFdqFtEfAQsFFEVP+IeaXuKUmSSlRFCzjkOtigH4y/LdubuqG88ygMPyEbJt5jWzjrVeg72D/ApaZmkz1gj/PhzQfhowl5p1GZyHN18b7AmymleTXaXwAC2AaYXvOiiKgAtgb+Wss9XwD2ioj2KaXPl/O91wXmAQuLeE9JkpS31u2zPbRbdcgK3pSKW/h+NgMe/hlMuAe69YF5M7Jec4eGS03XLmfC5vvCWn3yTqIykWdP9nrUUkRXa1u/juvWBNos59oo3LtWEbEJ8F1geEr/+Yh7le8ZEV0iolf1L2CDur6/JElqYO26QsvWMP1luOE7MO/fq3/PymXwwrVw+XZZj9Zuv8jmXq9R158rkpqMiKzArlwGo/7PhdC0QnkW2e2ARbW0L6z2el3XsSrXRkR74C5gPvDzYtwTOAuYVONrdB3nSpKkxlK5FGa8AkMPhUU1B87V04R74aH/gfX7wqnPwcBzS2dPbkmN47Pp2cKKNx8AU8fmnUYlLM8iewFZ73FNbau9Xtd11PfaiGgB3AFsARySUqrea71K9yy4BOhd42vXOs6VJEmNpcc3soWKZrwCw4+DZUvrd/2iefDWyOzxVw+Gw2+Ho++DbhsXP6uk0td5Azjq3uzxDd+BF69v2LUfVLbyLLKnU/sQ7Kq2aXVcN5usx7muaxO1D/u+FtgXODql9GSR7klKaU5KaXL1L2BKHdklSVJj2uw7sO/F8M4j8OCPVv4P4rdGwhU7wJ1HwqdTs0XVNt/Hhc2k5q7HtjDkKeg9MPudcu8QWOyyTfqyPIvs8cDmhf2pq9uhcHy5totSSpXAq0C/Wl7eAXin5gJlEfEH4DjgrJTSsGLcU5IklYl+x8Ou58C/boYpLy7/3E+nwh2D4fbDoU0nOPZB6NyjcXJKKg/t14QjhmVrM8x6ByLPkkqlKM93xHCgFXBiVUNEtCErhp9JKU0rtPWMiM1ruXbHiOhb7drNgN3J5lxTrf3HwP8A/5dSumwFeVbqnpIkqczsfh4c/whsuH3d57zzKFyxPbz7WLZlz5CnoOeOjZdRUvmoqMjWZjjhEWjVFma+DRNG5J1KJSJSjvMIImIYcBDwZ2AicAywHbBbSumZwjlPAANTSlHtuk7AOKADcDGwFPgRha2/UkofF847GLgHeAf4TS0R7k0pza/PPVfy5+oFTJo0aRK9evVa2cskSVJjGHN1tlLwJntkzxfPh9Yd4LOPYOS5sOevYc3euUaUVGZGnArjh8KOp8FeF0CLVnknUgObPHkyvXv3BuhdmDL8H3nukw1wNPDbwrEr8AqwT1WBXZeU0mcR8U2y4vw8sh75UWTDwasXw18vHPsAt9Ryq95kK43X556SJKlcLV0E426FTyZlwz3fuB/e+Hu2Ynin7nDYzXknlFSO9rsEWneEMVdkK48feqNb/DVjufZkN1X2ZEuSVMLmToPr9oK5U4CA7U7Ihoe3XSPvZJLK3avD4f4fQuv2cMj18JWBeSdSA1leT7az9CVJUvOyxvpw5N2w1WFw4mPZ6uMW2JKKYatBcPIoaLcmfDI57zTKSd7DxSVJkhrfOpvDIdfmnUJSU7T2ZtnCiS3bZM/H3wab7Q3tuuabS43GnmxJkiRJKqZWbSEC5nwAD5wJ1wyAaePyTqVGYpEtSZIkSQ2hS0849iGorITrvw1jbwLXxGryLLIlSZIkqaFsuF02fLzXLlmv9ohTYfHneadSA7LIliRJkqSG1KEbDB4OA38Kk5+GJQvyTqQGZJEtSZIkSQ2togXs9jM4bUxWdM+bCW8+lHcqNQCLbEmSJElqLK07ZMfRF8Md34dHfgnLluSbSUXlFl6SJEmS1Nj2ugAql8Czl8GUsXDojdBp3bxTqQjsyZYkSZKkxtayDex7MXz3Opg+Hq7eFSaNzjuVisAiW5IkSZLysvWhcNLj0LYzvPdE3mlUBA4XlyRJkqQ8rbMFnDwKWrXPnr/9CGy4PbTrkmssrRp7siVJkiQpb206ZSuQz/8Y7joW/joQpr+SdyqtAotsSZIkSSoVHbrBUffC0sVw/V7wr1vyTqR6ssiWJEmSpFLScwcY8hRsuAPcfzrcdxosWZB3Kq0ki2xJkiRJKjUd1856tAf8GN54AD6bkXcirSSLbEmSJEkqRRUtYPdfwhnjYM3esPhzmPh43qm0AhbZkiRJklTKOnTLjs9eCrccDP88H5YtzTeT6uQWXpIkSZJUDnY5Kxs2/swlMHUsHHI9dOqedyrVYE+2JEmSJJWDVm1h/0vgoKthyktwzQB4/9m8U6kGi2xJkiRJKifbfB9Oegxad4B//S3vNKrB4eKSJEmSVG66fxVOHgUVhZJuykuwVh9o2znfXLInW5IkSZLKUtvOWW/24vlw2/fgr9+EGa/lnarZs8iWJEmSpHLWugN879Zsi6/r9oTxt+WdqFmzyJYkSZKkcrfRTvCD0bBBPxhxCjxwJixZmHeqZskiW5IkSZKago7rwFEjoP+Pst7sf7+ed6JmySJbkiRJkpqKFi1hz/PhjLHQY1uoXAYfPJ93qmbFIluSJEmSmpouPbPjSzfADd+Cx36TFdxqcBbZkiRJktRU9T0Ktj0aRl8MtxwE82bmnajJs8iWJEmSpKaqVVs44DI48Er48AW4Zlf4YEzeqZo0i2xJkiRJaur6DoYTH4WWbeGJ/wcp5Z2oyWqZdwBJkiRJUiNYdysY8iQsXQwRMOsd6Ngd2q6Rd7ImxSJbkiRJkpqLtp2z47IlcNthEBVw2C3Qfct8czUhDheXJEmSpOamRatsrvbCuXDdHvDynXknajIssiVJkiSpOerVH34wGtbvC/eeDH//ESxdlHeqsmeRLUmSJEnNVad14ej7YecfwkvXw6TReScqexbZkiRJktSctWgJ3/otnPIc9Nkza/toQr6ZyphFtiRJkiTpi8XPJoyAq3aGxy+EymW5RipHFtmSJEmSpC/0+RZsMxie+j3cegjMn5V3orJikS1JkiRJ+kLr9nDgFbD/pfD+s3DNAPjwxbxTlQ2LbEmSJEnSl0XAN46BEx6BipbwwJlQWZl3qrLQMu8AkiRJkqQStf42MORJ+Hw2VFTA3OnQphO06Zh3spJlT7YkSZIkqW7tukK3jSElGHY0XLsb/PvNvFOVLItsSZIkSdKKRcAe58GCT+Da3eHV4XknKkkW2ZIkSZKkldN7AAwZDetuBXefAA/+DyxdlHeqkmKRLUmSJElaeWusB8f+HXY6HV68Fl67O+9EJcWFzyRJkiRJ9dOiFXz7Qth8P+i5Y9Y25wPo0jPfXCXAnmxJkiRJ0qrZaKdsrvb7z8KlfeGJi5r9Vl8W2ZIkSZKk1bPe1+Frg+CJ/4PbDs22/GqmLLIlSZIkSaundQc4+GrY7xKY9BRcMwCmjM07VS4ssiVJkiRJqy8C+h0Hx/8DCBh2VLNcedyFzyRJkiRJxdNjWxjyJMx5H1q2gYVzoaJF1tvdDNiTLUmSJEkqrvZrwvp9s8f3nQbX7g4z3843UyOxyJYkSZIkNZx+x8P8mXDtbjDh3rzTNDiLbEmSJElSw9l4NxgyGtbZEu46Fh7+GSxbkneqBmORLUmSJElqWJ17wLEPwg6nwJgr4flr8k7UYHItsiOiTURcFBHTImJBRIyJiD1W8toeETEsIuZExNyIGBERvWs57xcRcV9EzIiIFBG/ruN+NxVer/k1ZjV/TEmSJElSy9aw9+9g8N2w/clZ2/yP883UAPJeXfwm4BDgEuBd4FhgZEQMTCk9V9dFEdERGAV0Ai4ElgJnA09ExDYppU+qnf6/wEfAOOA7K8jzOTCkRtvMlfxZJEmSJEkr0mfP7PjvN+G6PaH/mdD/HKhoGgOtcyuyI2J74HDg7JTSJYW2vwGvARcBA5Zz+anAJsA3UkrjCteOLFx7NvCrauf2TilNjoguwCc1b1TDkpTSrfX/aSRJkiRJ9dJ5A9j02/D4/8KHL8DB12Srkpe5PD8qGAQsAa6rakgpLQSuB/pHxHoruHZMVYFduPZN4DHgsOonppQm1ydURLSIiE71uUaSJEmSVE9tOsIh18E+f4SJo+CagTD1X3mnWm15Ftl9gTdTSvNqtL8ABLBNbRdFRAWwNfBSLS+/AGwaEe1XMVMnYC4wNyJmRcSfIqLt8i6IiC4R0av6F7DBKn5/SZIkSWo+ImD7k+D4hyFVwi0HwcK5eadaLXnOyV4PmFpL+/TCcf06rlsTaFPtvJrXRuHeE+uZZzrwe7K52y2A/cmGnm8B7L2c684Czq/n95IkSZIkVdmgHwx5CqaPh7Zr5J1mteRZZLcDFtXSvrDa63VdxypeW6eU0s9qNN0eEVOAH0fEXimlf9Zx6SVkC7hVtwEwur4ZJEmSJKnZ6tANNlmpzaZKWp7DxReQ9UjX1Lba63VdxypeW18XF451/p9OKc1JKU2u/gVMKdL3lyRJkiSVkTyL7Olkw7prqmqbVsd1s8l6seu6NlH7UPJ6Syl9BCwmG6IuSZIkSdJy5Vlkjwc2L+x5Xd0OhePLtV2UUqoEXgX61fLyDsA7KaXPixEwIjYAWuNe2ZIkSZKklZBnkT0caAWcWNUQEW2A44BnUkrTCm09I2LzWq7dMSL6Vrt2M2B34K76BomItnVs23Ve4fiP+t5TkiRJktT85LbwWUrp+Yi4C/h9YU/sicAxwEbAsdVO/RswkGzV8CpXAicBD0XExcBS4Edkw8T/XP37RMRRhXtWzdceEBG/LDy+LKX0KbAuMC4ibgPeJPvwYX+yudh3ppSeKsoPLUmSJElq0vJcXRzgaOC3hWNX4BVgn5TSM8u7KKX0WUR8k6ygPo+sKB4FnJVS+rjG6SeQFelVdit8AdwKfArMAf4OfIuswK8A3gbOAf6yKj+YJEmSJKn5iZRS3hmanIjoBUyaNGkSvXr1yjmNJEmSJKmYJk+eTO/evQF6F3aY+o8852RLkiRJktSkWGRLkiRJklQkFtmSJEmSJBWJRbYkSZIkSUVikS1JkiRJUpFYZEuSJEmSVCQW2ZIkSZIkFYlFtiRJkiRJRWKRLUmSJElSkVhkS5IkSZJUJBbZkiRJkiQViUW2JEmSJElFYpEtSZIkSVKRtMw7QBPVAmDKlCl555AkSZIkFVm1Wq9FzdcipdS4aZqBiOgPjM47hyRJkiSpQe2aUnq6eoNFdgOIiDbAdsB0YFnOceqyAdkHAbsCdrmr3Pl+VlPi+1lNje9pNSW+n1WlBbAe8GJKaVH1Fxwu3gAK/5GfXuGJOYqIqodTUkqTc4wirTbfz2pKfD+rqfE9rabE97NqmFhbowufSZIkSZJUJBbZkiRJkiQViUW2JEmSJElFYpHdfM0BLigcpXI3B9/Pajrm4PtZTcscfE+r6ZiD72etgKuLS5IkSZJUJPZkS5IkSZJUJBbZkiRJkiQViUW2JEmSJElFYpHdjEREm4i4KCKmRcSCiBgTEXvknUtaFRGxXURcERGvR8T8iPggIu6IiE3yziYVQ0ScGxEpIsbnnUVaVYXf1Q9GxCcRMS8iXo6IY/POJdVXRPSJiDsjYkrh747XI+KnEdEm72wqPS581oxExO3AIcAlwLvAsUA/YGBK6bn8kkn1FxHDgV2Au4BXgHWB04EOwPYppTdyjCetlohYF3ib7MPwd1NK2+SbSKq/iNgbuA94ArgfWAJsCsxJKf02x2hSvURED+A14FPgamA2sCtwJHBrSumoHOOpBFlkNxMRsT3wPHB2SumSQltbsl8Y01JKA3KMJ9VbROwMvJRSWlytrQ/wKnBHSunYvLJJqysibgJ6khXZXSyyVW4iojPZB0V3pJTOzDuPtDoi4ifA74CvpZQmVGsfDhwItE8pLckrn0qPw8Wbj0FknyBfV9WQUloIXA/0j4j18gomrYqU0rPVC+xC2zvABGCLfFJJq6/woeiRwI/yziKthiOALsCvACKiU0REromkVbdG4fhRjfYZZH9fL2vcOCp1FtnNR1/gzZTSvBrtLwABbNPoiaQiK/wB1x2YlXcWaVUU3sOXATenlMbnHEdaHXsCbwL7RMSHwFxgdkT8LiJa5BtNqrcnC8frI+LrEbFhRAwmm3p5UUqpMr9oKkUt8w6gRrMeMLWW9umF4/qNmEVqKIOBHsAv8g4iraKjgS2Bg3LOIa2uTYANgZuA3wPjgP2AnwBtgbPyCibVV0rpkYg4D/g5cEC1l37l+gKqjUV289EOWFRL+8Jqr0tlKyI2B64AngZuyTmOVG8R0Ylszt/vUkrTV3S+VOI6Al2Bn6aULiq03RMRHYFTI+J/U0qOOlI5mUS2iN+9wMfAvsAFETEzpXR1nsFUeiyym48FQG1bDLSt9rpUlgorMT8IfAIc6rAtlalfAouBP+UdRCqCqr8rbq/RPhQ4FNgeeKhRE0mrKCIOB64BNk0pTSs03xMRFcAfI+LOlNIn+SVUqXFOdvMxnWzIeE1VbdNqeU0qeYUVbEcCnYFvp5Rm5BxJqrfC4pNnkY3G6B4RvSKiF9kHoa0Lz7vmGFGqr6rRGDUXiqp67vtZ5eRUYGy1ArvK/WRbh3698SOplFlkNx/jgc0Lw7Sq26FwfLlx40irr7AN3QNk+67ul1J6K+dI0qrqDrQGLiIbklj1tQPZavmTyOaySuVibOHYo0b7BoXjzEbMIq2u7kBtC/a1KhwdHawvschuPoaT/SI4saohItoAxwHP1PLJnFTSCqvT3gnsRDZEfEzOkaTVMQk4uJavCcDkwuO/5RVOWgV3FY4nVDUUVs8/EZgP+Dtb5eRtoF9EbFyj/ftk23e90viRVMoipZR3BjWSiBhGtmLtn4GJwDHAdsBuKaVncowm1VtEXAKcSdaTPazGy/NSSiMaO5NUbBHxBNAlpbRNzlGkeouIm4GjgOuBf5EtFLUvcG5K6Q95ZpPqIyIGAI+TbRF6OTCbbLX8vYGrU0qn5BhPJcgiuxkpDK39LXAk2VyoV4Cfp5QezTWYtAoKxcfAOl5+P6XUq/HSSA3DIlvlLCJaA+eRfai/LvAe8OeU0jW5BpNWQURsD/wa6At0IxuBdCPwh5TSshyjqQRZZEuSJEmSVCTOyZYkSZIkqUgssiVJkiRJKhKLbEmSJEmSisQiW5IkSZKkIrHIliRJkiSpSCyyJUmSJEkqEotsSZIkSZKKxCJbkiSVnYhIEXFT3jkkSarJIluSpCYgIu6LiEeqPX8oIu7JM5MkSc2RRbYkSU3DzsDTABFRUXj+bK6JJElqhiyyJUkqcxGxKbAW8EyhaSugMxbZkiQ1OotsSZLKUER0jIi1ImItYE+gEphYeL4XsBR4v3BO55W8Z+eIuCgi3o2IRRExMyJuj4iv1Djv2MKc6D0j4tcR8X7h/Fci4vA67n1QRDwTEfMjYl7h8YF1nNs3Iu6KiI8K9/2wkGPjWs7dKSKeLNz344i4LiI6rszPK0lSQ2iZdwBJkrRKLgeOqdE2qcbzKYXjk8A3l3ezQiH+LNATuAGYAKwHnAo8HxH9Ukrv17jsIqADcGXh+XHA7RHRNqV0U7V7nwpcAbwJ/KbQfCwwIiKGpJT+Wu3c/YC7gfnAdcC7wLrAt4GvAROrff9tgL8DNwK3FX7GE8g+cDh5eT+vJEkNJVJKeWeQJEn1FBFbAusXnt5FVmzeXHg+ArgduLPw/JOU0tgV3O8vZIXpjimll6u1bwS8CtyTUjq20HYsWWH7AbB1SunTQntn4BWgE9AjpbQgIroCHwIzgG1TSnML564BjAPWATZMKc2JiPbA+0AC+qaUptbIWJFSqiw8ToXzdkopPV/tnAeBbwFdU0rzlvczS5LUEBwuLklSGUopvZ5SepSsgO0C3Fx4/m+y3uUbU0qPFr5WVGAHMBh4CphaNQy9MPR8PjCGrHCt6aqqAruQ6VPgaqArX/Sc71XIc2lVgV04dy5wKdCRbLg7ZL3VawEX1yywC9dU1mh6rnqBXfA42Ui9Xsv7mSVJaigOF5ckqcwU5hy3LTw9CFgEvFEoivcnK4yr5mcvXIke3bWBbmSF9Mw6zqlZ4AK8UUvb64Vj1Tzu3oXjhFrOnVDj3D6F47g6k37Ze7W0fVw4dlvJe0iSVFQW2ZIklZ/a5mNPqfF8RuF4M9n85+WJwvFRsnnW5WLZcl6L5bwmSVKDsciWJKn8/B64tfD4PrJC+h6gBdnc7CuBBwqvT1uJ+80E5gBrFIacr6wtCt+/ui0Lx/dqHL8KPLaCc98uHLcBHqlHDkmSSoZzsiVJKjPV5mNPA9oDQwvPZ5N9gP63avOxX1/evQr3qwSGAttHxKDazomIdWppPqX69mCFxz8gK9ifLDT/k2z4+hkR0anauZ2AM4B5hXMgK6xnAedExHq1ZLB3WpJU8uzJliSpfA0EFgAvFp4PAOYC41fhXr8AdgGGRcQwssXOFgMbAfsAY/nvYeezyLb3urHw/DiyLcBOTCl9DlBYNfxcsi28no+ImwrnHgtsAgypWjwtpfR5RJwADAdei4iqLbzWJlsU7U/8d8+5JEklxSJbkqTyNRB4PqW0uPB8APBsSml5c5VrlVL6NCJ2Ac4BDgMOBJaSzfV+mmzP6pp+AuwKnAZ0JxvuPTildFuNe18ZEdOBHwPnF5pfBg5OKY2oce79EdEf+DnZntedgI+A0WRbiUmSVNLcJ1uSJNVLtX2yd0spPZFvGkmSSotzsiVJkiRJKhKLbEmSJEmSisQiW5IkSZKkInFOtiRJkiRJRWJPtiRJkiRJRWKRLUmSJElSkVhkS5IkSZJUJBbZkiRJkiQViUW2JEmSJElFYpEtSZIkSVKR/H9xAkePESP0yAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show plot accuracy changes during training\n",
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "sns.lineplot(data=(history.history[\"loss\"], history.history[\"val_loss\"]))\n",
    "\n",
    "plt.title('LSTM Loss across epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('# epoch')\n",
    "plt.legend(['train', 'test'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3556aa9",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "0affc9a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence_index = 280"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "11019d73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"mesurement_cycle_number\", \"target_cycle_number\"\n",
    "\n",
    "input_cycles = data[\"test\"][\"mesurement_cycle_number\"].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "c84c1fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_sequence = data[\"test\"][\"x_test\"][sequence_index,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "3e3b89a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_seq_plot = {\n",
    "    \"x\": input_cycles.iloc[sequence_index][1],\n",
    "    \"y\": np.trim_zeros(input_sequence).flatten()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "1fce5e85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "sns.lineplot(data=input_seq_plot, x=\"x\", y=\"y\")\n",
    "\n",
    "plt.title('Input Sequence')\n",
    "plt.ylabel('Capacity (Ah)')\n",
    "plt.xlabel('Cycle Index')\n",
    "plt.legend(['Capacity'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "e06a966c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_target = data[\"test\"][\"y_test\"][sequence_index,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "3c19082f",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_cycles = data[\"test\"][\"target_cycle_number\"].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "f906d534",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_seq_plot = {\n",
    "    \"x\": output_cycles.iloc[sequence_index][1],\n",
    "    \"y\": np.trim_zeros(y_target).flatten()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "3835ca35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "sns.lineplot(data=output_seq_plot, x=\"x\", y=\"y\")\n",
    "\n",
    "plt.title('Target Sequence')\n",
    "plt.ylabel('Capacity (Ah)')\n",
    "plt.xlabel('Cycle Index')\n",
    "plt.legend(['Capacity'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da3b0fc8",
   "metadata": {},
   "source": [
    "### Make Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "f816be0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = autoencoder.predict(data[\"test\"][\"x_test\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "841998ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1083, 39, 1)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "c5888ff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = y_pred[sequence_index,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "c40040ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.7158251 ],\n",
       "       [1.7093377 ],\n",
       "       [1.6983371 ],\n",
       "       [1.68665   ],\n",
       "       [1.6749125 ],\n",
       "       [1.6661179 ],\n",
       "       [1.655056  ],\n",
       "       [1.6433125 ],\n",
       "       [1.6316221 ],\n",
       "       [1.6196933 ],\n",
       "       [1.6070118 ],\n",
       "       [1.5940969 ],\n",
       "       [1.5813628 ],\n",
       "       [1.5666633 ],\n",
       "       [1.5502479 ],\n",
       "       [1.5308493 ],\n",
       "       [1.5035564 ],\n",
       "       [1.475002  ],\n",
       "       [1.4432094 ],\n",
       "       [1.4055865 ],\n",
       "       [1.3648767 ],\n",
       "       [1.3158517 ],\n",
       "       [1.2569611 ],\n",
       "       [1.1953709 ],\n",
       "       [1.1339173 ],\n",
       "       [1.0768689 ],\n",
       "       [1.0270901 ],\n",
       "       [0.9922414 ],\n",
       "       [0.9658569 ],\n",
       "       [0.9454192 ],\n",
       "       [0.9297326 ],\n",
       "       [0.917827  ],\n",
       "       [0.90824187],\n",
       "       [0.90049773],\n",
       "       [0.8942188 ],\n",
       "       [0.8891101 ],\n",
       "       [0.8851954 ],\n",
       "       [0.88220966],\n",
       "       [0.88011193]], dtype=float32)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "259056b7",
   "metadata": {},
   "source": [
    "# Plot Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f63c6e34",
   "metadata": {},
   "source": [
    "The length of the prediction is a fixed length. Cut the prediction to the length of the target sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "5fa57b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_pred = np.trim_zeros(prediction.flatten())[:len(np.trim_zeros(y_target.flatten()))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "b3662a16",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_data = {\n",
    "    \"cycles\": output_cycles.iloc[sequence_index][1],\n",
    "    \"target\": np.trim_zeros(y_target.flatten()),\n",
    "    \"prediction\": plot_pred\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "962c3885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Battery Cycle'), Text(0, 0.5, 'Battery Capacity (Ah)')]"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "fig.suptitle(\"Comparing target values to predictions\")\n",
    "\n",
    "palette = sns.color_palette(\"flare\", 1)\n",
    "sns.color_palette(\"rocket\")\n",
    "sns.scatterplot(\n",
    "    data=plot_data,\n",
    "    x=\"cycles\",\n",
    "    y=\"target\",\n",
    "    legend=\"full\"\n",
    ")\n",
    "sns.scatterplot(\n",
    "    data=plot_data,\n",
    "    x=\"cycles\",\n",
    "    y=\"prediction\",\n",
    "    legend=\"full\"\n",
    ")\n",
    "plt.legend(labels=[\"Prediction\", \"Target\"])\n",
    "\n",
    "ax.set(xlabel='Battery Cycle', ylabel='Battery Capacity (Ah)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "ee233298",
   "metadata": {},
   "outputs": [],
   "source": [
    "seq = np.trim_zeros(input_sequence.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "d335620a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = plot_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "34c7e177",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_cycles = data[\"test\"][\"mesurement_cycle_number\"].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "c999f611",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_cycles = input_cycles.iloc[sequence_index][1] + output_cycles.iloc[sequence_index][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "a63c1d16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_cycles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "861f719c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full = np.concatenate([seq, pred])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "bfe9ba9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "cb4f09f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_data = {\n",
    "    \"cycles\": all_cycles,\n",
    "    \"capacity\": full\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "03a1f065",
   "metadata": {},
   "outputs": [],
   "source": [
    "## target\n",
    "foo_x = np.trim_zeros(data[\"test\"][\"x_test\"][sequence_index,:,:].flatten())\n",
    "foo_y = np.trim_zeros(data[\"test\"][\"y_test\"][sequence_index,:,:].flatten())\n",
    "target = np.concatenate([foo_x, foo_y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "78a67495",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_data = {\n",
    "    \"cycles\": all_cycles,\n",
    "    \"capacity\": target\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "4e689706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Battery Cycle'), Text(0, 0.5, 'Battery Capacity (Ah)')]"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "fig.suptitle(\"Comparing target values to predictions\")\n",
    "\n",
    "palette = sns.color_palette(\"flare\", 1)\n",
    "sns.color_palette(\"rocket\")\n",
    "sns.scatterplot(\n",
    "    data=full_data,\n",
    "    x=\"cycles\",\n",
    "    y=\"capacity\"\n",
    ")\n",
    "\n",
    "sns.scatterplot(\n",
    "    data=target_data,\n",
    "    x=\"cycles\",\n",
    "    y=\"capacity\"\n",
    ")\n",
    "\n",
    "plt.legend(labels=[\"Prediction\", \"Target\"])\n",
    "\n",
    "ax.set(xlabel='Battery Cycle', ylabel='Battery Capacity (Ah)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a1fd181",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "poetry-kernel",
   "language": "python",
   "name": "poetry-kernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}